Overview

Dataset statistics

Number of variables55
Number of observations166043
Missing cells1504864
Missing cells (%)16.5%
Duplicate rows135
Duplicate rows (%)0.1%
Total size in memory390.1 MiB
Average record size in memory2.4 KiB

Variable types

Categorical34
Numeric20
Unsupported1

Alerts

SG_UF has constant value "RJ"Constant
Dataset has 135 (0.1%) duplicate rowsDuplicates
DT_PRESTACAO_CONTAS has a high cardinality: 871 distinct valuesHigh cardinality
NM_UE has a high cardinality: 92 distinct valuesHigh cardinality
NM_CANDIDATO has a high cardinality: 23651 distinct valuesHigh cardinality
NM_DOADOR has a high cardinality: 47664 distinct valuesHigh cardinality
NM_DOADOR_RFB has a high cardinality: 43540 distinct valuesHigh cardinality
NM_MUNICIPIO_DOADOR has a high cardinality: 95 distinct valuesHigh cardinality
NR_RECIBO_DOACAO has a high cardinality: 93722 distinct valuesHigh cardinality
NR_DOCUMENTO_DOACAO has a high cardinality: 36402 distinct valuesHigh cardinality
DT_RECEITA has a high cardinality: 430 distinct valuesHigh cardinality
DS_RECEITA has a high cardinality: 38701 distinct valuesHigh cardinality
CD_ELEICAO is highly overall correlated with ANO_ELEICAO and 14 other fieldsHigh correlation
SQ_PRESTADOR_CONTAS is highly overall correlated with ANO_ELEICAO and 20 other fieldsHigh correlation
NR_CNPJ_PRESTADOR_CONTA is highly overall correlated with ANO_ELEICAO and 20 other fieldsHigh correlation
CD_CARGO is highly overall correlated with ANO_ELEICAO and 15 other fieldsHigh correlation
SQ_CANDIDATO is highly overall correlated with ANO_ELEICAO and 23 other fieldsHigh correlation
NR_CANDIDATO is highly overall correlated with NM_UE and 10 other fieldsHigh correlation
NR_CPF_VICE_CANDIDATO is highly overall correlated with ST_TURNO and 15 other fieldsHigh correlation
NR_PARTIDO is highly overall correlated with NM_UE and 9 other fieldsHigh correlation
CD_ORIGEM_RECEITA is highly overall correlated with NM_UE and 9 other fieldsHigh correlation
CD_ESPECIE_RECEITA is highly overall correlated with CD_ORIGEM_RECEITA and 7 other fieldsHigh correlation
CD_CNAE_DOADOR is highly overall correlated with DS_ORIGEM_RECEITA and 1 other fieldsHigh correlation
NR_CPF_CNPJ_DOADOR is highly overall correlated with ANO_ELEICAO and 24 other fieldsHigh correlation
CD_MUNICIPIO_DOADOR is highly overall correlated with CD_ESFERA_PARTIDARIA_DOADOR and 3 other fieldsHigh correlation
SQ_CANDIDATO_DOADOR is highly overall correlated with ANO_ELEICAO and 12 other fieldsHigh correlation
NR_CANDIDATO_DOADOR is highly overall correlated with NR_CANDIDATO and 16 other fieldsHigh correlation
CD_CARGO_CANDIDATO_DOADOR is highly overall correlated with ANO_ELEICAO and 26 other fieldsHigh correlation
NR_PARTIDO_DOADOR is highly overall correlated with NM_UE and 10 other fieldsHigh correlation
SQ_RECEITA is highly overall correlated with ANO_ELEICAO and 20 other fieldsHigh correlation
VR_RECEITA is highly overall correlated with NM_MUNICIPIO_DOADOR and 1 other fieldsHigh correlation
ANO_ELEICAO is highly overall correlated with CD_ELEICAO and 20 other fieldsHigh correlation
CD_TIPO_ELEICAO is highly overall correlated with NM_TIPO_ELEICAO and 7 other fieldsHigh correlation
NM_TIPO_ELEICAO is highly overall correlated with CD_TIPO_ELEICAO and 7 other fieldsHigh correlation
DS_ELEICAO is highly overall correlated with ANO_ELEICAO and 20 other fieldsHigh correlation
DT_ELEICAO is highly overall correlated with ANO_ELEICAO and 20 other fieldsHigh correlation
ST_TURNO is highly overall correlated with NR_CPF_VICE_CANDIDATOHigh correlation
NM_UE is highly overall correlated with ANO_ELEICAO and 27 other fieldsHigh correlation
DS_CARGO is highly overall correlated with ANO_ELEICAO and 15 other fieldsHigh correlation
SG_PARTIDO is highly overall correlated with ANO_ELEICAO and 18 other fieldsHigh correlation
NM_PARTIDO is highly overall correlated with ANO_ELEICAO and 21 other fieldsHigh correlation
CD_FONTE_RECEITA is highly overall correlated with DS_FONTE_RECEITA and 7 other fieldsHigh correlation
DS_FONTE_RECEITA is highly overall correlated with CD_FONTE_RECEITA and 7 other fieldsHigh correlation
DS_ORIGEM_RECEITA is highly overall correlated with CD_FONTE_RECEITA and 13 other fieldsHigh correlation
CD_NATUREZA_RECEITA is highly overall correlated with CD_ORIGEM_RECEITA and 8 other fieldsHigh correlation
DS_NATUREZA_RECEITA is highly overall correlated with CD_ORIGEM_RECEITA and 8 other fieldsHigh correlation
DS_ESPECIE_RECEITA is highly overall correlated with ANO_ELEICAO and 9 other fieldsHigh correlation
DS_CNAE_DOADOR is highly overall correlated with DS_ORIGEM_RECEITA and 1 other fieldsHigh correlation
CD_ESFERA_PARTIDARIA_DOADOR is highly overall correlated with ANO_ELEICAO and 19 other fieldsHigh correlation
DS_ESFERA_PARTIDARIA_DOADOR is highly overall correlated with ANO_ELEICAO and 19 other fieldsHigh correlation
SG_UF_DOADOR is highly overall correlated with CD_ESFERA_PARTIDARIA_DOADOR and 6 other fieldsHigh correlation
NM_MUNICIPIO_DOADOR is highly overall correlated with NM_UE and 23 other fieldsHigh correlation
DS_CARGO_CANDIDATO_DOADOR is highly overall correlated with ANO_ELEICAO and 26 other fieldsHigh correlation
SG_PARTIDO_DOADOR is highly overall correlated with ANO_ELEICAO and 24 other fieldsHigh correlation
NM_PARTIDO_DOADOR is highly overall correlated with ANO_ELEICAO and 26 other fieldsHigh correlation
NR_CPF_CANDIDATO is highly overall correlated with NR_CPF_VICE_CANDIDATOHigh correlation
NR_CPF_VICE_CANDIDATO has 154995 (93.3%) missing valuesMissing
CD_CNAE_DOADOR has 65274 (39.3%) missing valuesMissing
DS_CNAE_DOADOR has 65274 (39.3%) missing valuesMissing
CD_ESFERA_PARTIDARIA_DOADOR has 129053 (77.7%) missing valuesMissing
DS_ESFERA_PARTIDARIA_DOADOR has 129053 (77.7%) missing valuesMissing
SG_UF_DOADOR has 50163 (30.2%) missing valuesMissing
CD_MUNICIPIO_DOADOR has 89465 (53.9%) missing valuesMissing
NM_MUNICIPIO_DOADOR has 89465 (53.9%) missing valuesMissing
SQ_CANDIDATO_DOADOR has 88401 (53.2%) missing valuesMissing
NR_CANDIDATO_DOADOR has 87152 (52.5%) missing valuesMissing
CD_CARGO_CANDIDATO_DOADOR has 87152 (52.5%) missing valuesMissing
DS_CARGO_CANDIDATO_DOADOR has 87152 (52.5%) missing valuesMissing
NR_PARTIDO_DOADOR has 50202 (30.2%) missing valuesMissing
SG_PARTIDO_DOADOR has 50202 (30.2%) missing valuesMissing
NM_PARTIDO_DOADOR has 50202 (30.2%) missing valuesMissing
NR_RECIBO_DOACAO has 64522 (38.9%) missing valuesMissing
NR_DOCUMENTO_DOACAO has 104954 (63.2%) missing valuesMissing
DS_RECEITA has 61517 (37.0%) missing valuesMissing
CD_MUNICIPIO_DOADOR is highly skewed (γ1 = -26.43915831)Skewed
SQ_CANDIDATO_DOADOR is highly skewed (γ1 = 97.13432083)Skewed
VR_RECEITA is highly skewed (γ1 = 49.42185627)Skewed
NR_RECIBO_DOACAO is uniformly distributedUniform
SG_UE is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2022-12-14 14:35:39.804957
Analysis finished2022-12-14 14:38:17.751724
Duration2 minutes and 37.95 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

ANO_ELEICAO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
2020
112455 
2018
33804 
2022
19784 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters664172
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2020 112455
67.7%
2018 33804
 
20.4%
2022 19784
 
11.9%

Length

2022-12-14T11:38:18.087827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:18.203730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 112455
67.7%
2018 33804
 
20.4%
2022 19784
 
11.9%

Most occurring characters

ValueCountFrequency (%)
2 318066
47.9%
0 278498
41.9%
1 33804
 
5.1%
8 33804
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 664172
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 318066
47.9%
0 278498
41.9%
1 33804
 
5.1%
8 33804
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 664172
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 318066
47.9%
0 278498
41.9%
1 33804
 
5.1%
8 33804
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 664172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 318066
47.9%
0 278498
41.9%
1 33804
 
5.1%
8 33804
 
5.1%

CD_TIPO_ELEICAO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
2
165651 
1
 
392

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 165651
99.8%
1 392
 
0.2%

Length

2022-12-14T11:38:18.305224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:18.408428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 165651
99.8%
1 392
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 165651
99.8%
1 392
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166043
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 165651
99.8%
1 392
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 166043
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 165651
99.8%
1 392
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 165651
99.8%
1 392
 
0.2%

NM_TIPO_ELEICAO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.2 MiB
ORDINÁRIA
165651 
SUPLEMENTAR
 
392

Length

Max length11
Median length9
Mean length9.0047217
Min length9

Characters and Unicode

Total characters1495171
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowORDINÁRIA
2nd rowORDINÁRIA
3rd rowORDINÁRIA
4th rowORDINÁRIA
5th rowORDINÁRIA

Common Values

ValueCountFrequency (%)
ORDINÁRIA 165651
99.8%
SUPLEMENTAR 392
 
0.2%

Length

2022-12-14T11:38:18.499580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:18.614755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ordinária 165651
99.8%
suplementar 392
 
0.2%

Most occurring characters

ValueCountFrequency (%)
R 331694
22.2%
I 331302
22.2%
N 166043
11.1%
A 166043
11.1%
O 165651
11.1%
D 165651
11.1%
Á 165651
11.1%
E 784
 
0.1%
S 392
 
< 0.1%
U 392
 
< 0.1%
Other values (4) 1568
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1495171
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 331694
22.2%
I 331302
22.2%
N 166043
11.1%
A 166043
11.1%
O 165651
11.1%
D 165651
11.1%
Á 165651
11.1%
E 784
 
0.1%
S 392
 
< 0.1%
U 392
 
< 0.1%
Other values (4) 1568
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1495171
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 331694
22.2%
I 331302
22.2%
N 166043
11.1%
A 166043
11.1%
O 165651
11.1%
D 165651
11.1%
Á 165651
11.1%
E 784
 
0.1%
S 392
 
< 0.1%
U 392
 
< 0.1%
Other values (4) 1568
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1329520
88.9%
None 165651
 
11.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 331694
24.9%
I 331302
24.9%
N 166043
12.5%
A 166043
12.5%
O 165651
12.5%
D 165651
12.5%
E 784
 
0.1%
S 392
 
< 0.1%
U 392
 
< 0.1%
P 392
 
< 0.1%
Other values (3) 1176
 
0.1%
None
ValueCountFrequency (%)
Á 165651
100.0%

CD_ELEICAO
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean414.22376
Minimum297
Maximum551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:18.689476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum297
5-th percentile297
Q1426
median426
Q3426
95-th percentile546
Maximum551
Range254
Interquartile range (IQR)0

Descriptive statistics

Standard deviation70.583869
Coefficient of variation (CV)0.17040034
Kurtosis0.007695057
Mean414.22376
Median Absolute Deviation (MAD)0
Skewness-0.14736964
Sum68778955
Variance4982.0825
MonotonicityNot monotonic
2022-12-14T11:38:18.773552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
426 112063
67.5%
297 33804
 
20.4%
546 19784
 
11.9%
508 112
 
0.1%
459 62
 
< 0.1%
551 61
 
< 0.1%
506 49
 
< 0.1%
507 44
 
< 0.1%
532 39
 
< 0.1%
458 25
 
< 0.1%
ValueCountFrequency (%)
297 33804
 
20.4%
426 112063
67.5%
458 25
 
< 0.1%
459 62
 
< 0.1%
506 49
 
< 0.1%
507 44
 
< 0.1%
508 112
 
0.1%
532 39
 
< 0.1%
546 19784
 
11.9%
551 61
 
< 0.1%
ValueCountFrequency (%)
551 61
 
< 0.1%
546 19784
 
11.9%
532 39
 
< 0.1%
508 112
 
0.1%
507 44
 
< 0.1%
506 49
 
< 0.1%
459 62
 
< 0.1%
458 25
 
< 0.1%
426 112063
67.5%
297 33804
 
20.4%

DS_ELEICAO
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.9 MiB
Eleições Municipais 2020
112063 
Eleições Gerais Estaduais 2018
33804 
Eleições Gerais Estaduais 2022
19784 
RJ - Suplementar de Silva Jardim
 
112
RJ - Suplementar de Sta. Ma. Madalena
 
111
Other values (3)
 
169

Length

Max length37
Median length24
Mean length25.954957
Min length24

Characters and Unicode

Total characters4309639
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEleições Gerais Estaduais 2022
2nd rowEleições Gerais Estaduais 2022
3rd rowEleições Gerais Estaduais 2022
4th rowEleições Gerais Estaduais 2022
5th rowEleições Gerais Estaduais 2022

Common Values

ValueCountFrequency (%)
Eleições Municipais 2020 112063
67.5%
Eleições Gerais Estaduais 2018 33804
 
20.4%
Eleições Gerais Estaduais 2022 19784
 
11.9%
RJ - Suplementar de Silva Jardim 112
 
0.1%
RJ - Suplementar de Sta. Ma. Madalena 111
 
0.1%
RJ - Suplementar de Itatiaia 105
 
0.1%
RJ - Suplementar de Carapebus 39
 
< 0.1%
RJ - Suplementar de Itatiaia 25
 
< 0.1%

Length

2022-12-14T11:38:18.898757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:19.054202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
eleições 165651
30.0%
municipais 112063
20.3%
2020 112063
20.3%
gerais 53588
 
9.7%
estaduais 53588
 
9.7%
2018 33804
 
6.1%
2022 19784
 
3.6%
de 392
 
0.1%
suplementar 392
 
0.1%
392
 
0.1%
Other values (8) 1118
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 609500
14.1%
s 438517
10.2%
386817
 
9.0%
e 386216
 
9.0%
2 317282
 
7.4%
0 277714
 
6.4%
a 274466
 
6.4%
E 219239
 
5.1%
l 166266
 
3.9%
u 166082
 
3.9%
Other values (22) 1067540
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2872812
66.7%
Decimal Number 662604
 
15.4%
Space Separator 386817
 
9.0%
Uppercase Letter 386792
 
9.0%
Dash Punctuation 392
 
< 0.1%
Other Punctuation 222
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 609500
21.2%
s 438517
15.3%
e 386216
13.4%
a 274466
9.6%
l 166266
 
5.8%
u 166082
 
5.8%
õ 165651
 
5.8%
ç 165651
 
5.8%
n 112566
 
3.9%
p 112494
 
3.9%
Other values (7) 275403
9.6%
Uppercase Letter
ValueCountFrequency (%)
E 219239
56.7%
M 112285
29.0%
G 53588
 
13.9%
S 615
 
0.2%
J 504
 
0.1%
R 392
 
0.1%
I 130
 
< 0.1%
C 39
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 317282
47.9%
0 277714
41.9%
1 33804
 
5.1%
8 33804
 
5.1%
Space Separator
ValueCountFrequency (%)
386817
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 392
100.0%
Other Punctuation
ValueCountFrequency (%)
. 222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3259604
75.6%
Common 1050035
 
24.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 609500
18.7%
s 438517
13.5%
e 386216
11.8%
a 274466
8.4%
E 219239
 
6.7%
l 166266
 
5.1%
u 166082
 
5.1%
õ 165651
 
5.1%
ç 165651
 
5.1%
n 112566
 
3.5%
Other values (15) 555450
17.0%
Common
ValueCountFrequency (%)
386817
36.8%
2 317282
30.2%
0 277714
26.4%
1 33804
 
3.2%
8 33804
 
3.2%
- 392
 
< 0.1%
. 222
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3978337
92.3%
None 331302
 
7.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 609500
15.3%
s 438517
11.0%
386817
9.7%
e 386216
9.7%
2 317282
8.0%
0 277714
 
7.0%
a 274466
 
6.9%
E 219239
 
5.5%
l 166266
 
4.2%
u 166082
 
4.2%
Other values (20) 736238
18.5%
None
ValueCountFrequency (%)
õ 165651
50.0%
ç 165651
50.0%

DT_ELEICAO
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
15/11/2020
112063 
07/10/2018
33804 
02/10/2022
19784 
12/09/2021
 
205
11/04/2021
 
87
Other values (2)
 
100

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1660430
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02/10/2022
2nd row02/10/2022
3rd row02/10/2022
4th row02/10/2022
5th row02/10/2022

Common Values

ValueCountFrequency (%)
15/11/2020 112063
67.5%
07/10/2018 33804
 
20.4%
02/10/2022 19784
 
11.9%
12/09/2021 205
 
0.1%
11/04/2021 87
 
0.1%
13/03/2022 61
 
< 0.1%
07/11/2021 39
 
< 0.1%

Length

2022-12-14T11:38:19.194084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:19.327107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
15/11/2020 112063
67.5%
07/10/2018 33804
 
20.4%
02/10/2022 19784
 
11.9%
12/09/2021 205
 
0.1%
11/04/2021 87
 
0.1%
13/03/2022 61
 
< 0.1%
07/11/2021 39
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 424430
25.6%
0 385674
23.2%
2 338116
20.4%
/ 332086
20.0%
5 112063
 
6.7%
7 33843
 
2.0%
8 33804
 
2.0%
9 205
 
< 0.1%
3 122
 
< 0.1%
4 87
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1328344
80.0%
Other Punctuation 332086
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 424430
32.0%
0 385674
29.0%
2 338116
25.5%
5 112063
 
8.4%
7 33843
 
2.5%
8 33804
 
2.5%
9 205
 
< 0.1%
3 122
 
< 0.1%
4 87
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 332086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1660430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 424430
25.6%
0 385674
23.2%
2 338116
20.4%
/ 332086
20.0%
5 112063
 
6.7%
7 33843
 
2.0%
8 33804
 
2.0%
9 205
 
< 0.1%
3 122
 
< 0.1%
4 87
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1660430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 424430
25.6%
0 385674
23.2%
2 338116
20.4%
/ 332086
20.0%
5 112063
 
6.7%
7 33843
 
2.0%
8 33804
 
2.0%
9 205
 
< 0.1%
3 122
 
< 0.1%
4 87
 
< 0.1%

ST_TURNO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
1
165134 
2
 
909

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 165134
99.5%
2 909
 
0.5%

Length

2022-12-14T11:38:19.444708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:19.556695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 165134
99.5%
2 909
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 165134
99.5%
2 909
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166043
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 165134
99.5%
2 909
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 166043
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 165134
99.5%
2 909
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 165134
99.5%
2 909
 
0.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.8 MiB
FINAL
164059 
PARCIAL
 
1304
RELATÓRIO FINANCEIRO
 
577
REGULARIZAÇÃO DA OMISSÃO
 
103

Length

Max length24
Median length5
Mean length5.0796179
Min length5

Characters and Unicode

Total characters843435
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFINAL
2nd rowFINAL
3rd rowFINAL
4th rowFINAL
5th rowFINAL

Common Values

ValueCountFrequency (%)
FINAL 164059
98.8%
PARCIAL 1304
 
0.8%
RELATÓRIO FINANCEIRO 577
 
0.3%
REGULARIZAÇÃO DA OMISSÃO 103
 
0.1%

Length

2022-12-14T11:38:19.647216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:19.769227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
final 164059
98.3%
parcial 1304
 
0.8%
relatório 577
 
0.3%
financeiro 577
 
0.3%
regularização 103
 
0.1%
da 103
 
0.1%
omissão 103
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 168130
19.9%
I 167300
19.8%
L 166043
19.7%
N 165213
19.6%
F 164636
19.5%
R 3241
 
0.4%
C 1881
 
0.2%
O 1463
 
0.2%
P 1304
 
0.2%
E 1257
 
0.1%
Other values (11) 2967
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 842652
99.9%
Space Separator 783
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 168130
20.0%
I 167300
19.9%
L 166043
19.7%
N 165213
19.6%
F 164636
19.5%
R 3241
 
0.4%
C 1881
 
0.2%
O 1463
 
0.2%
P 1304
 
0.2%
E 1257
 
0.1%
Other values (10) 2184
 
0.3%
Space Separator
ValueCountFrequency (%)
783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 842652
99.9%
Common 783
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 168130
20.0%
I 167300
19.9%
L 166043
19.7%
N 165213
19.6%
F 164636
19.5%
R 3241
 
0.4%
C 1881
 
0.2%
O 1463
 
0.2%
P 1304
 
0.2%
E 1257
 
0.1%
Other values (10) 2184
 
0.3%
Common
ValueCountFrequency (%)
783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 842549
99.9%
None 886
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 168130
20.0%
I 167300
19.9%
L 166043
19.7%
N 165213
19.6%
F 164636
19.5%
R 3241
 
0.4%
C 1881
 
0.2%
O 1463
 
0.2%
P 1304
 
0.2%
E 1257
 
0.1%
Other values (8) 2081
 
0.2%
None
ValueCountFrequency (%)
Ó 577
65.1%
à 206
 
23.3%
Ç 103
 
11.6%
Distinct871
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
15/12/2020
23241 
14/12/2020
 
10951
01/11/2022
 
7423
11/12/2020
 
6799
06/11/2018
 
6397
Other values (866)
111232 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1660430
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)< 0.1%

Sample

1st row17/11/2022
2nd row17/11/2022
3rd row17/11/2022
4th row17/11/2022
5th row17/11/2022

Common Values

ValueCountFrequency (%)
15/12/2020 23241
 
14.0%
14/12/2020 10951
 
6.6%
01/11/2022 7423
 
4.5%
11/12/2020 6799
 
4.1%
06/11/2018 6397
 
3.9%
10/12/2020 4914
 
3.0%
13/12/2020 4425
 
2.7%
12/12/2020 4406
 
2.7%
31/10/2022 3285
 
2.0%
09/12/2020 3161
 
1.9%
Other values (861) 91041
54.8%

Length

2022-12-14T11:38:19.864502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15/12/2020 23241
 
14.0%
14/12/2020 10951
 
6.6%
01/11/2022 7423
 
4.5%
11/12/2020 6799
 
4.1%
06/11/2018 6397
 
3.9%
10/12/2020 4914
 
3.0%
13/12/2020 4425
 
2.7%
12/12/2020 4406
 
2.7%
31/10/2022 3285
 
2.0%
09/12/2020 3161
 
1.9%
Other values (861) 91041
54.8%

Most occurring characters

ValueCountFrequency (%)
2 449524
27.1%
0 364257
21.9%
1 332316
20.0%
/ 332086
20.0%
8 37666
 
2.3%
5 36065
 
2.2%
3 31027
 
1.9%
9 24975
 
1.5%
4 22890
 
1.4%
6 16629
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1328344
80.0%
Other Punctuation 332086
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 449524
33.8%
0 364257
27.4%
1 332316
25.0%
8 37666
 
2.8%
5 36065
 
2.7%
3 31027
 
2.3%
9 24975
 
1.9%
4 22890
 
1.7%
6 16629
 
1.3%
7 12995
 
1.0%
Other Punctuation
ValueCountFrequency (%)
/ 332086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1660430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 449524
27.1%
0 364257
21.9%
1 332316
20.0%
/ 332086
20.0%
8 37666
 
2.3%
5 36065
 
2.2%
3 31027
 
1.9%
9 24975
 
1.5%
4 22890
 
1.4%
6 16629
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1660430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 449524
27.1%
0 364257
21.9%
1 332316
20.0%
/ 332086
20.0%
8 37666
 
2.3%
5 36065
 
2.2%
3 31027
 
1.9%
9 24975
 
1.5%
4 22890
 
1.4%
6 16629
 
1.0%

SQ_PRESTADOR_CONTAS
Real number (ℝ)

Distinct25363
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7866964 × 109
Minimum4.1626776 × 108
Maximum3.8725941 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:19.979533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4.1626776 × 108
5-th percentile4.1949243 × 108
Q11.828968 × 109
median1.8441777 × 109
Q31.8476245 × 109
95-th percentile3.7861522 × 109
Maximum3.8725941 × 109
Range3.4563263 × 109
Interquartile range (IQR)18656572

Descriptive statistics

Standard deviation9.2527563 × 108
Coefficient of variation (CV)0.51786953
Kurtosis0.53094585
Mean1.7866964 × 109
Median Absolute Deviation (MAD)5666945
Skewness0.54074664
Sum2.9666843 × 1014
Variance8.5613498 × 1017
MonotonicityNot monotonic
2022-12-14T11:38:20.095645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1834939732 459
 
0.3%
1843176159 330
 
0.2%
3781005024 289
 
0.2%
1846427020 208
 
0.1%
420246912 206
 
0.1%
1845560244 185
 
0.1%
416988212 175
 
0.1%
1846980129 172
 
0.1%
1829968303 168
 
0.1%
422524176 168
 
0.1%
Other values (25353) 163683
98.6%
ValueCountFrequency (%)
416267760 34
< 0.1%
416267792 1
 
< 0.1%
416267818 6
 
< 0.1%
416267841 1
 
< 0.1%
416267842 16
 
< 0.1%
416267843 54
< 0.1%
416267936 2
 
< 0.1%
416267937 8
 
< 0.1%
416267956 5
 
< 0.1%
416267968 46
< 0.1%
ValueCountFrequency (%)
3872594057 1
 
< 0.1%
3872224213 1
 
< 0.1%
3872224194 2
 
< 0.1%
3872224103 3
 
< 0.1%
3871480915 5
< 0.1%
3869614225 2
 
< 0.1%
3858814809 10
< 0.1%
3858069456 1
 
< 0.1%
3858069430 1
 
< 0.1%
3858069422 2
 
< 0.1%

SG_UF
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.3 MiB
RJ
166043 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters332086
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRJ
2nd rowRJ
3rd rowRJ
4th rowRJ
5th rowRJ

Common Values

ValueCountFrequency (%)
RJ 166043
100.0%

Length

2022-12-14T11:38:20.207717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:20.304453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
rj 166043
100.0%

Most occurring characters

ValueCountFrequency (%)
R 166043
50.0%
J 166043
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 332086
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 166043
50.0%
J 166043
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 332086
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 166043
50.0%
J 166043
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 166043
50.0%
J 166043
50.0%

SG_UE
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size6.9 MiB

NM_UE
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct92
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
RIO DE JANEIRO
66233 
CAMPOS DOS GOYTACAZES
 
6287
NITERÓI
 
4491
BARRA MANSA
 
3641
PORTO REAL
 
3065
Other values (87)
82326 

Length

Max length29
Median length27
Mean length12.549032
Min length4

Characters and Unicode

Total characters2083679
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRIO DE JANEIRO
2nd rowRIO DE JANEIRO
3rd rowRIO DE JANEIRO
4th rowRIO DE JANEIRO
5th rowRIO DE JANEIRO

Common Values

ValueCountFrequency (%)
RIO DE JANEIRO 66233
39.9%
CAMPOS DOS GOYTACAZES 6287
 
3.8%
NITERÓI 4491
 
2.7%
BARRA MANSA 3641
 
2.2%
PORTO REAL 3065
 
1.8%
BELFORD ROXO 3053
 
1.8%
SÃO GONÇALO 2831
 
1.7%
VOLTA REDONDA 2701
 
1.6%
NOVA FRIBURGO 2280
 
1.4%
MAGÉ 2269
 
1.4%
Other values (82) 69192
41.7%

Length

2022-12-14T11:38:20.394769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 73508
18.9%
rio 70190
18.0%
janeiro 66233
17.0%
são 9079
 
2.3%
dos 8759
 
2.3%
goytacazes 6287
 
1.6%
campos 6287
 
1.6%
barra 6113
 
1.6%
do 4800
 
1.2%
niterói 4491
 
1.2%
Other values (124) 133479
34.3%

Most occurring characters

ValueCountFrequency (%)
O 249840
12.0%
A 235940
11.3%
R 229281
11.0%
223183
10.7%
I 206045
9.9%
E 202124
9.7%
D 116852
 
5.6%
N 103738
 
5.0%
S 80998
 
3.9%
J 72229
 
3.5%
Other values (25) 363449
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1860310
89.3%
Space Separator 223183
 
10.7%
Dash Punctuation 186
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 249840
13.4%
A 235940
12.7%
R 229281
12.3%
I 206045
11.1%
E 202124
10.9%
D 116852
 
6.3%
N 103738
 
5.6%
S 80998
 
4.4%
J 72229
 
3.9%
T 42959
 
2.3%
Other values (23) 320304
17.2%
Space Separator
ValueCountFrequency (%)
223183
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1860310
89.3%
Common 223369
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 249840
13.4%
A 235940
12.7%
R 229281
12.3%
I 206045
11.1%
E 202124
10.9%
D 116852
 
6.3%
N 103738
 
5.6%
S 80998
 
4.4%
J 72229
 
3.9%
T 42959
 
2.3%
Other values (23) 320304
17.2%
Common
ValueCountFrequency (%)
223183
99.9%
- 186
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2033697
97.6%
None 49982
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 249840
12.3%
A 235940
11.6%
R 229281
11.3%
223183
11.0%
I 206045
10.1%
E 202124
9.9%
D 116852
 
5.7%
N 103738
 
5.1%
S 80998
 
4.0%
J 72229
 
3.6%
Other values (16) 313467
15.4%
None
ValueCountFrequency (%)
à 14148
28.3%
Ó 9789
19.6%
Ç 6565
13.1%
É 6552
13.1%
Í 5823
11.7%
Á 3499
 
7.0%
Ê 1724
 
3.4%
Ú 1197
 
2.4%
Ô 685
 
1.4%

NR_CNPJ_PRESTADOR_CONTA
Real number (ℝ)

Distinct25355
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8331065 × 1013
Minimum3.1123042 × 1013
Maximum4.7957406 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:20.505553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.1123042 × 1013
5-th percentile3.1181598 × 1013
Q13.8528763 × 1013
median3.8788057 × 1013
Q33.9085447 × 1013
95-th percentile4.750905 × 1013
Maximum4.7957406 × 1013
Range1.6834364 × 1013
Interquartile range (IQR)5.56684 × 1011

Descriptive statistics

Standard deviation4.5347826 × 1012
Coefficient of variation (CV)0.11830568
Kurtosis0.21804079
Mean3.8331065 × 1013
Median Absolute Deviation (MAD)2.74865 × 1011
Skewness0.19139016
Sum6.364605 × 1018
Variance2.0564254 × 1025
MonotonicityNot monotonic
2022-12-14T11:38:20.613552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38575762000199 459
 
0.3%
38667216000188 330
 
0.2%
47475432000112 289
 
0.2%
38913167000116 208
 
0.1%
31192807000105 206
 
0.1%
38865091000109 185
 
0.1%
31149858000146 175
 
0.1%
38966997000101 172
 
0.1%
38533570000110 168
 
0.1%
31212058000122 168
 
0.1%
Other values (25345) 163683
98.6%
ValueCountFrequency (%)
31123042000143 5
 
< 0.1%
31123046000121 4
 
< 0.1%
31123047000176 7
 
< 0.1%
31123049000165 7
 
< 0.1%
31123050000190 26
 
< 0.1%
31123051000134 25
 
< 0.1%
31123053000123 3
 
< 0.1%
31123054000178 73
< 0.1%
31123055000112 15
 
< 0.1%
31123056000167 14
 
< 0.1%
ValueCountFrequency (%)
47957406000120 5
< 0.1%
47934024000181 1
 
< 0.1%
47924205000127 3
 
< 0.1%
47924195000120 2
 
< 0.1%
47924186000139 1
 
< 0.1%
47906471000127 2
 
< 0.1%
47810290000100 2
 
< 0.1%
47798546000101 10
< 0.1%
47791875000112 1
 
< 0.1%
47791873000123 3
 
< 0.1%

CD_CARGO
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.805075
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:20.714566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q17
median13
Q313
95-th percentile13
Maximum13
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.9960778
Coefficient of variation (CV)0.27728432
Kurtosis-1.2478029
Mean10.805075
Median Absolute Deviation (MAD)0
Skewness-0.75907341
Sum1794107
Variance8.9764821
MonotonicityNot monotonic
2022-12-14T11:38:20.788560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
13 102984
62.0%
7 32877
 
19.8%
6 19111
 
11.5%
11 9468
 
5.7%
3 837
 
0.5%
5 763
 
0.5%
12 3
 
< 0.1%
ValueCountFrequency (%)
3 837
 
0.5%
5 763
 
0.5%
6 19111
 
11.5%
7 32877
 
19.8%
11 9468
 
5.7%
12 3
 
< 0.1%
13 102984
62.0%
ValueCountFrequency (%)
13 102984
62.0%
12 3
 
< 0.1%
11 9468
 
5.7%
7 32877
 
19.8%
6 19111
 
11.5%
5 763
 
0.5%
3 837
 
0.5%

DS_CARGO
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.7 MiB
Vereador
102984 
Deputado Estadual
32877 
Deputado Federal
19111 
Prefeito
 
9468
Governador
 
837
Other values (2)
 
766

Length

Max length17
Median length8
Mean length10.708377
Min length7

Characters and Unicode

Total characters1778051
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeputado Estadual
2nd rowDeputado Estadual
3rd rowDeputado Estadual
4th rowDeputado Estadual
5th rowDeputado Estadual

Common Values

ValueCountFrequency (%)
Vereador 102984
62.0%
Deputado Estadual 32877
 
19.8%
Deputado Federal 19111
 
11.5%
Prefeito 9468
 
5.7%
Governador 837
 
0.5%
Senador 763
 
0.5%
Vice-prefeito 3
 
< 0.1%

Length

2022-12-14T11:38:20.875559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:20.984553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
vereador 102984
47.2%
deputado 51988
23.8%
estadual 32877
 
15.1%
federal 19111
 
8.8%
prefeito 9468
 
4.3%
governador 837
 
0.4%
senador 763
 
0.3%
vice-prefeito 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 316723
17.8%
a 241437
13.6%
r 236987
13.3%
d 208560
11.7%
o 166880
9.4%
V 102987
 
5.8%
t 94336
 
5.3%
u 84865
 
4.8%
p 51991
 
2.9%
D 51988
 
2.9%
Other values (14) 221297
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1508029
84.8%
Uppercase Letter 218031
 
12.3%
Space Separator 51988
 
2.9%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 316723
21.0%
a 241437
16.0%
r 236987
15.7%
d 208560
13.8%
o 166880
11.1%
t 94336
 
6.3%
u 84865
 
5.6%
p 51991
 
3.4%
l 51988
 
3.4%
s 32877
 
2.2%
Other values (5) 21385
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
V 102987
47.2%
D 51988
23.8%
E 32877
 
15.1%
F 19111
 
8.8%
P 9468
 
4.3%
G 837
 
0.4%
S 763
 
0.3%
Space Separator
ValueCountFrequency (%)
51988
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1726060
97.1%
Common 51991
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 316723
18.3%
a 241437
14.0%
r 236987
13.7%
d 208560
12.1%
o 166880
9.7%
V 102987
 
6.0%
t 94336
 
5.5%
u 84865
 
4.9%
p 51991
 
3.0%
D 51988
 
3.0%
Other values (12) 169306
9.8%
Common
ValueCountFrequency (%)
51988
> 99.9%
- 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1778051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 316723
17.8%
a 241437
13.6%
r 236987
13.3%
d 208560
11.7%
o 166880
9.4%
V 102987
 
5.8%
t 94336
 
5.3%
u 84865
 
4.8%
p 51991
 
2.9%
D 51988
 
2.9%
Other values (14) 221297
12.4%

SQ_CANDIDATO
Real number (ℝ)

Distinct25363
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9000095 × 1011
Minimum1.900006 × 1011
Maximum1.9000174 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:21.102090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.900006 × 1011
5-th percentile1.9000061 × 1011
Q11.9000066 × 1011
median1.9000087 × 1011
Q31.9000112 × 1011
95-th percentile1.9000162 × 1011
Maximum1.9000174 × 1011
Range1138171
Interquartile range (IQR)465869

Descriptive statistics

Standard deviation320193.07
Coefficient of variation (CV)1.6852182 × 10-6
Kurtosis-0.1314178
Mean1.9000095 × 1011
Median Absolute Deviation (MAD)229486
Skewness0.88625643
Sum3.1548328 × 1016
Variance1.025236 × 1011
MonotonicityNot monotonic
2022-12-14T11:38:21.219093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190000688286 459
 
0.3%
190000782184 330
 
0.2%
190001612455 289
 
0.2%
190001009042 208
 
0.1%
190000607467 206
 
0.1%
190000989813 185
 
0.1%
190000602265 175
 
0.1%
190001055012 172
 
0.1%
190000662122 168
 
0.1%
190000613606 168
 
0.1%
Other values (25353) 163683
98.6%
ValueCountFrequency (%)
190000601071 20
< 0.1%
190000601072 4
 
< 0.1%
190000601073 6
 
< 0.1%
190000601074 2
 
< 0.1%
190000601075 8
 
< 0.1%
190000601076 16
< 0.1%
190000601077 5
 
< 0.1%
190000601078 29
< 0.1%
190000601079 3
 
< 0.1%
190000601081 15
< 0.1%
ValueCountFrequency (%)
190001739242 1
 
< 0.1%
190001738883 1
 
< 0.1%
190001738882 2
 
< 0.1%
190001738867 3
 
< 0.1%
190001738698 5
< 0.1%
190001737551 2
 
< 0.1%
190001732486 10
< 0.1%
190001732307 3
 
< 0.1%
190001732306 1
 
< 0.1%
190001732304 1
 
< 0.1%

NR_CANDIDATO
Real number (ℝ)

Distinct8737
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27491.573
Minimum10
Maximum90999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:21.344009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile55
Q111333
median20696
Q343007
95-th percentile77123
Maximum90999
Range90989
Interquartile range (IQR)31674

Descriptive statistics

Standard deviation22855.179
Coefficient of variation (CV)0.83135216
Kurtosis0.1851681
Mean27491.573
Median Absolute Deviation (MAD)11606
Skewness0.98553792
Sum4.5647833 × 109
Variance5.223592 × 108
MonotonicityNot monotonic
2022-12-14T11:38:21.469008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 1028
 
0.6%
55 1019
 
0.6%
77 855
 
0.5%
20 676
 
0.4%
12 553
 
0.3%
10 488
 
0.3%
22 464
 
0.3%
10123 462
 
0.3%
19 458
 
0.3%
11111 450
 
0.3%
Other values (8727) 159590
96.1%
ValueCountFrequency (%)
10 488
0.3%
11 450
0.3%
12 553
0.3%
13 242
0.1%
14 162
 
0.1%
15 440
0.3%
16 107
 
0.1%
17 165
 
0.1%
18 36
 
< 0.1%
19 458
0.3%
ValueCountFrequency (%)
90999 167
0.1%
90991 3
 
< 0.1%
90990 39
 
< 0.1%
90979 2
 
< 0.1%
90977 7
 
< 0.1%
90964 3
 
< 0.1%
90963 1
 
< 0.1%
90951 8
 
< 0.1%
90949 3
 
< 0.1%
90922 5
 
< 0.1%

NM_CANDIDATO
Categorical

Distinct23651
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
EDUARDO DA COSTA PAES
 
512
ALESSANDRO CRONGE BOUZADA
 
330
ROMÁRIO DE SOUZA FARIA
 
289
MAX RODRIGUES LEMOS
 
262
FABIANO GONÇALVES
 
258
Other values (23646)
164392 

Length

Max length59
Median length49
Mean length25.481743
Min length8

Characters and Unicode

Total characters4231065
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3285 ?
Unique (%)2.0%

Sample

1st rowFILIPE BEZERRA RIBEIRO SOARES
2nd rowFILIPE BEZERRA RIBEIRO SOARES
3rd rowFILIPE BEZERRA RIBEIRO SOARES
4th rowFILIPE BEZERRA RIBEIRO SOARES
5th rowFILIPE BEZERRA RIBEIRO SOARES

Common Values

ValueCountFrequency (%)
EDUARDO DA COSTA PAES 512
 
0.3%
ALESSANDRO CRONGE BOUZADA 330
 
0.2%
ROMÁRIO DE SOUZA FARIA 289
 
0.2%
MAX RODRIGUES LEMOS 262
 
0.2%
FABIANO GONÇALVES 258
 
0.2%
PAULO GUSTAVO GANIME ALVES TEIXEIRA 246
 
0.1%
WLADIMIR BARROS ASSED MATHEUS DE OLIVEIRA 235
 
0.1%
SERGIO ALBERTO SOARES 223
 
0.1%
FELIPE DOS SANTOS PEIXOTO 217
 
0.1%
DANIELA MOTE DE SOUZA CARNEIRO 217
 
0.1%
Other values (23641) 163254
98.3%

Length

2022-12-14T11:38:21.598009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 45513
 
6.9%
da 32344
 
4.9%
silva 28790
 
4.4%
santos 13759
 
2.1%
oliveira 12522
 
1.9%
souza 12052
 
1.8%
dos 10734
 
1.6%
carlos 7254
 
1.1%
pereira 6558
 
1.0%
luiz 6436
 
1.0%
Other values (9503) 479707
73.2%

Most occurring characters

ValueCountFrequency (%)
A 543867
12.9%
489664
11.6%
E 378856
9.0%
O 358857
 
8.5%
R 335219
 
7.9%
I 334395
 
7.9%
S 274348
 
6.5%
L 217294
 
5.1%
N 203360
 
4.8%
D 195896
 
4.6%
Other values (33) 899309
21.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3741392
88.4%
Space Separator 489664
 
11.6%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 543867
14.5%
E 378856
10.1%
O 358857
9.6%
R 335219
9.0%
I 334395
8.9%
S 274348
 
7.3%
L 217294
 
5.8%
N 203360
 
5.4%
D 195896
 
5.2%
C 120133
 
3.2%
Other values (31) 779167
20.8%
Space Separator
ValueCountFrequency (%)
489664
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3741392
88.4%
Common 489673
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 543867
14.5%
E 378856
10.1%
O 358857
9.6%
R 335219
9.0%
I 334395
8.9%
S 274348
 
7.3%
L 217294
 
5.8%
N 203360
 
5.4%
D 195896
 
5.2%
C 120133
 
3.2%
Other values (31) 779167
20.8%
Common
ValueCountFrequency (%)
489664
> 99.9%
, 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4201258
99.3%
None 29807
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 543867
12.9%
489664
11.7%
E 378856
9.0%
O 358857
8.5%
R 335219
 
8.0%
I 334395
 
8.0%
S 274348
 
6.5%
L 217294
 
5.2%
N 203360
 
4.8%
D 195896
 
4.7%
Other values (18) 869502
20.7%
None
ValueCountFrequency (%)
É 7497
25.2%
à 6614
22.2%
Ç 6239
20.9%
Á 3175
10.7%
Í 1303
 
4.4%
Ú 1284
 
4.3%
Ô 1119
 
3.8%
Ê 920
 
3.1%
Ó 707
 
2.4%
 528
 
1.8%
Other values (5) 421
 
1.4%

NR_CPF_CANDIDATO
Real number (ℝ)

Distinct23390
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6220773 × 1010
Minimum1078755
Maximum9.9999448 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:21.720014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1078755
5-th percentile8.4640073 × 108
Q14.7550208 × 109
median9.9119678 × 109
Q34.7224755 × 1010
95-th percentile9.0671954 × 1010
Maximum9.9999448 × 1010
Range9.9998369 × 1010
Interquartile range (IQR)4.2469734 × 1010

Descriptive statistics

Standard deviation3.1101281 × 1010
Coefficient of variation (CV)1.1861314
Kurtosis-0.31308293
Mean2.6220773 × 1010
Median Absolute Deviation (MAD)6.909882 × 109
Skewness1.1426602
Sum4.3537757 × 1015
Variance9.6728969 × 1020
MonotonicityNot monotonic
2022-12-14T11:38:22.032005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1475189702 512
 
0.3%
90671953753 341
 
0.2%
3087414638 330
 
0.2%
75061600720 262
 
0.2%
2652630776 258
 
0.2%
69049351468 255
 
0.2%
9911967751 246
 
0.1%
10855834730 235
 
0.1%
13531689720 223
 
0.1%
1290538727 217
 
0.1%
Other values (23380) 163164
98.3%
ValueCountFrequency (%)
1078755 1
 
< 0.1%
3496740 7
 
< 0.1%
3512703 4
 
< 0.1%
4772733 27
< 0.1%
6186750 1
 
< 0.1%
6616780 2
 
< 0.1%
6674712 1
 
< 0.1%
7487738 41
< 0.1%
8084742 1
 
< 0.1%
8596727 7
 
< 0.1%
ValueCountFrequency (%)
99999447791 2
 
< 0.1%
99997339720 1
 
< 0.1%
99975688772 8
< 0.1%
99974428734 8
< 0.1%
99969114700 6
< 0.1%
99964635753 1
 
< 0.1%
99962454700 4
< 0.1%
99961920678 3
 
< 0.1%
99961580710 4
< 0.1%
99961482700 5
< 0.1%

NR_CPF_VICE_CANDIDATO
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct614
Distinct (%)5.6%
Missing154995
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean2.8498865 × 1010
Minimum16568729
Maximum9.9750016 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:22.150002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16568729
5-th percentile7.5734371 × 108
Q16.608914 × 109
median1.2351218 × 1010
Q35.0478847 × 1010
95-th percentile8.7518211 × 1010
Maximum9.9750016 × 1010
Range9.9733447 × 1010
Interquartile range (IQR)4.3869933 × 1010

Descriptive statistics

Standard deviation2.9507191 × 1010
Coefficient of variation (CV)1.0353813
Kurtosis-0.51871641
Mean2.8498865 × 1010
Median Absolute Deviation (MAD)1.0489053 × 1010
Skewness0.93769881
Sum3.1485546 × 1014
Variance8.7067434 × 1020
MonotonicityNot monotonic
2022-12-14T11:38:22.271438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38676508704.0 459
 
0.3%
12820072704.0 330
 
0.2%
1862164754.0 289
 
0.2%
64112462620.0 208
 
0.1%
70649146700.0 185
 
0.1%
12817257731.0 172
 
0.1%
51538903768.0 168
 
0.1%
10643846760.0 150
 
0.1%
21290946787.0 136
 
0.1%
8927071786.0 132
 
0.1%
Other values (604) 8819
 
5.3%
(Missing) 154995
93.3%
ValueCountFrequency (%)
16568729.0 4
 
< 0.1%
34745777.0 9
 
< 0.1%
138178763.0 8
 
< 0.1%
160714761.0 2
 
< 0.1%
189539720.0 2
 
< 0.1%
209623713.0 7
 
< 0.1%
243984707.0 5
 
< 0.1%
249712784.0 15
 
< 0.1%
260155799.0 40
< 0.1%
270287710.0 5
 
< 0.1%
ValueCountFrequency (%)
99750015720.0 18
< 0.1%
99590638791.0 6
 
< 0.1%
99488957700.0 43
< 0.1%
99485370768.0 22
< 0.1%
99171104704.0 9
 
< 0.1%
98950622734.0 11
 
< 0.1%
98271016768.0 16
 
< 0.1%
98087436768.0 13
 
< 0.1%
97406120753.0 35
< 0.1%
97192465704.0 7
 
< 0.1%

NR_PARTIDO
Real number (ℝ)

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.960113
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:22.379435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q115
median25
Q345
95-th percentile77
Maximum90
Range80
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.538171
Coefficient of variation (CV)0.65346168
Kurtosis0.040595461
Mean32.960113
Median Absolute Deviation (MAD)12
Skewness1.0088607
Sum5472796
Variance463.89279
MonotonicityNot monotonic
2022-12-14T11:38:22.482443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
55 9947
 
6.0%
10 8914
 
5.4%
22 8275
 
5.0%
25 8059
 
4.9%
20 7994
 
4.8%
15 7542
 
4.5%
77 7522
 
4.5%
11 7400
 
4.5%
17 7132
 
4.3%
12 6993
 
4.2%
Other values (26) 86265
52.0%
ValueCountFrequency (%)
10 8914
5.4%
11 7400
4.5%
12 6993
4.2%
13 6769
4.1%
14 5622
3.4%
15 7542
4.5%
16 352
 
0.2%
17 7132
4.3%
18 1737
 
1.0%
19 4372
2.6%
ValueCountFrequency (%)
90 5015
3.0%
80 193
 
0.1%
77 7522
4.5%
70 5382
3.2%
65 3163
 
1.9%
55 9947
6.0%
54 283
 
0.2%
51 4673
2.8%
50 5212
3.1%
45 5244
3.2%

SG_PARTIDO
Categorical

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
PSD
 
9947
DEM
 
8059
PSC
 
7994
MDB
 
7542
PL
 
7530
Other values (36)
124971 

Length

Max length13
Median length12
Mean length4.4030944
Min length2

Characters and Unicode

Total characters731103
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNIÃO
2nd rowUNIÃO
3rd rowUNIÃO
4th rowUNIÃO
5th rowUNIÃO

Common Values

ValueCountFrequency (%)
PSD 9947
 
6.0%
DEM 8059
 
4.9%
PSC 7994
 
4.8%
MDB 7542
 
4.5%
PL 7530
 
4.5%
SOLIDARIEDADE 7522
 
4.5%
PP 7400
 
4.5%
REPUBLICANOS 7313
 
4.4%
PSL 7132
 
4.3%
PDT 6993
 
4.2%
Other values (31) 88611
53.4%

Length

2022-12-14T11:38:22.593445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
psd 9947
 
5.8%
dem 8059
 
4.7%
psc 7994
 
4.6%
mdb 7542
 
4.4%
pl 7530
 
4.4%
solidariedade 7522
 
4.4%
pp 7400
 
4.3%
republicanos 7313
 
4.2%
psl 7132
 
4.1%
pdt 6993
 
4.1%
Other values (33) 94937
55.1%

Most occurring characters

ValueCountFrequency (%)
P 130227
17.8%
D 83733
11.5%
A 61963
8.5%
S 61501
8.4%
E 43644
 
6.0%
B 43115
 
5.9%
T 42306
 
5.8%
I 41079
 
5.6%
O 40462
 
5.5%
L 34992
 
4.8%
Other values (12) 148081
20.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 718451
98.3%
Space Separator 6326
 
0.9%
Lowercase Letter 6326
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 130227
18.1%
D 83733
11.7%
A 61963
8.6%
S 61501
8.6%
E 43644
 
6.1%
B 43115
 
6.0%
T 42306
 
5.9%
I 41079
 
5.7%
O 40462
 
5.6%
L 34992
 
4.9%
Other values (9) 135429
18.9%
Lowercase Letter
ValueCountFrequency (%)
d 3163
50.0%
o 3163
50.0%
Space Separator
ValueCountFrequency (%)
6326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 724777
99.1%
Common 6326
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 130227
18.0%
D 83733
11.6%
A 61963
8.5%
S 61501
8.5%
E 43644
 
6.0%
B 43115
 
5.9%
T 42306
 
5.8%
I 41079
 
5.7%
O 40462
 
5.6%
L 34992
 
4.8%
Other values (11) 141755
19.6%
Common
ValueCountFrequency (%)
6326
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 730173
99.9%
None 930
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 130227
17.8%
D 83733
11.5%
A 61963
8.5%
S 61501
8.4%
E 43644
 
6.0%
B 43115
 
5.9%
T 42306
 
5.8%
I 41079
 
5.6%
O 40462
 
5.5%
L 34992
 
4.8%
Other values (11) 147151
20.2%
None
ValueCountFrequency (%)
à 930
100.0%

NM_PARTIDO
Categorical

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.4 MiB
Partido Social Democrático
 
9947
Democratas
 
8059
Partido Social Cristão
 
7994
Movimento Democrático Brasileiro
 
7542
Partido Liberal
 
7530
Other values (37)
124971 

Length

Max length46
Median length31
Mean length21.50985
Min length4

Characters and Unicode

Total characters3571560
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNIÃO BRASIL
2nd rowUNIÃO BRASIL
3rd rowUNIÃO BRASIL
4th rowUNIÃO BRASIL
5th rowUNIÃO BRASIL

Common Values

ValueCountFrequency (%)
Partido Social Democrático 9947
 
6.0%
Democratas 8059
 
4.9%
Partido Social Cristão 7994
 
4.8%
Movimento Democrático Brasileiro 7542
 
4.5%
Partido Liberal 7530
 
4.5%
Solidariedade 7522
 
4.5%
REPUBLICANOS 7313
 
4.4%
Partido Social Liberal 7132
 
4.3%
Partido Democrático Trabalhista 6993
 
4.2%
Partido dos Trabalhadores 6769
 
4.1%
Other values (32) 89242
53.7%

Length

2022-12-14T11:38:22.693075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
partido 99659
24.0%
social 35332
 
8.5%
democrático 24482
 
5.9%
brasileiro 24062
 
5.8%
trabalhista 20457
 
4.9%
da 17513
 
4.2%
liberal 14662
 
3.5%
cristão 10947
 
2.6%
democracia 9763
 
2.3%
brasileira 8577
 
2.1%
Other values (41) 150133
36.1%

Most occurring characters

ValueCountFrequency (%)
a 428311
12.0%
i 382252
 
10.7%
o 342446
 
9.6%
r 315209
 
8.8%
249544
 
7.0%
d 206178
 
5.8%
t 200084
 
5.6%
e 178198
 
5.0%
l 150872
 
4.2%
c 133282
 
3.7%
Other values (34) 985184
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2745016
76.9%
Uppercase Letter 577000
 
16.2%
Space Separator 249544
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 428311
15.6%
i 382252
13.9%
o 342446
12.5%
r 315209
11.5%
d 206178
7.5%
t 200084
7.3%
e 178198
6.5%
l 150872
 
5.5%
c 133282
 
4.9%
s 121577
 
4.4%
Other values (14) 286607
10.4%
Uppercase Letter
ValueCountFrequency (%)
P 125489
21.7%
S 90036
15.6%
B 44045
 
7.6%
D 42304
 
7.3%
T 39427
 
6.8%
R 36536
 
6.3%
C 32511
 
5.6%
L 28400
 
4.9%
A 25839
 
4.5%
O 19826
 
3.4%
Other values (9) 92587
16.0%
Space Separator
ValueCountFrequency (%)
249544
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3322016
93.0%
Common 249544
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 428311
12.9%
i 382252
11.5%
o 342446
 
10.3%
r 315209
 
9.5%
d 206178
 
6.2%
t 200084
 
6.0%
e 178198
 
5.4%
l 150872
 
4.5%
c 133282
 
4.0%
P 125489
 
3.8%
Other values (33) 859695
25.9%
Common
ValueCountFrequency (%)
249544
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3525131
98.7%
None 46429
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 428311
12.2%
i 382252
10.8%
o 342446
 
9.7%
r 315209
 
8.9%
249544
 
7.1%
d 206178
 
5.8%
t 200084
 
5.7%
e 178198
 
5.1%
l 150872
 
4.3%
c 133282
 
3.8%
Other values (29) 938755
26.6%
None
ValueCountFrequency (%)
á 24866
53.6%
ã 17677
38.1%
ç 2211
 
4.8%
à 930
 
2.0%
ú 745
 
1.6%

CD_FONTE_RECEITA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
1
102948 
2
53939 
0
 
9156

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166043
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 102948
62.0%
2 53939
32.5%
0 9156
 
5.5%

Length

2022-12-14T11:38:22.787332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:22.890943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 102948
62.0%
2 53939
32.5%
0 9156
 
5.5%

Most occurring characters

ValueCountFrequency (%)
1 102948
62.0%
2 53939
32.5%
0 9156
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166043
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 102948
62.0%
2 53939
32.5%
0 9156
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 166043
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 102948
62.0%
2 53939
32.5%
0 9156
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 102948
62.0%
2 53939
32.5%
0 9156
 
5.5%

DS_FONTE_RECEITA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
OUTROS RECURSOS
102948 
FUNDO ESPECIAL
53939 
FUNDO PARTIDARIO
 
9156

Length

Max length16
Median length15
Mean length14.730293
Min length14

Characters and Unicode

Total characters2445862
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOUTROS RECURSOS
2nd rowOUTROS RECURSOS
3rd rowOUTROS RECURSOS
4th rowOUTROS RECURSOS
5th rowOUTROS RECURSOS

Common Values

ValueCountFrequency (%)
OUTROS RECURSOS 102948
62.0%
FUNDO ESPECIAL 53939
32.5%
FUNDO PARTIDARIO 9156
 
5.5%

Length

2022-12-14T11:38:22.983927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:23.098933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
outros 102948
31.0%
recursos 102948
31.0%
fundo 63095
19.0%
especial 53939
16.2%
partidario 9156
 
2.8%

Most occurring characters

ValueCountFrequency (%)
O 381095
15.6%
S 362783
14.8%
R 327156
13.4%
U 268991
11.0%
E 210826
8.6%
166043
6.8%
C 156887
6.4%
T 112104
 
4.6%
D 72251
 
3.0%
I 72251
 
3.0%
Other values (5) 315475
12.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2279819
93.2%
Space Separator 166043
 
6.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 381095
16.7%
S 362783
15.9%
R 327156
14.4%
U 268991
11.8%
E 210826
9.2%
C 156887
6.9%
T 112104
 
4.9%
D 72251
 
3.2%
I 72251
 
3.2%
A 72251
 
3.2%
Other values (4) 243224
10.7%
Space Separator
ValueCountFrequency (%)
166043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2279819
93.2%
Common 166043
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 381095
16.7%
S 362783
15.9%
R 327156
14.4%
U 268991
11.8%
E 210826
9.2%
C 156887
6.9%
T 112104
 
4.9%
D 72251
 
3.2%
I 72251
 
3.2%
A 72251
 
3.2%
Other values (4) 243224
10.7%
Common
ValueCountFrequency (%)
166043
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2445862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 381095
15.6%
S 362783
14.8%
R 327156
13.4%
U 268991
11.0%
E 210826
8.6%
166043
6.8%
C 156887
6.4%
T 112104
 
4.6%
D 72251
 
3.0%
I 72251
 
3.0%
Other values (5) 315475
12.9%

CD_ORIGEM_RECEITA
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10023583
Minimum10010100
Maximum10040000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:23.180934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10010100
5-th percentile10010100
Q110010200
median10020000
Q310040000
95-th percentile10040000
Maximum10040000
Range29900
Interquartile range (IQR)29800

Descriptive statistics

Standard deviation13167.918
Coefficient of variation (CV)0.0013136937
Kurtosis-1.6817441
Mean10023583
Median Absolute Deviation (MAD)9800
Skewness0.30007093
Sum1.6643457 × 1012
Variance1.7339406 × 108
MonotonicityNot monotonic
2022-12-14T11:38:23.259938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
10040000 61543
37.1%
10010200 47657
28.7%
10020000 36989
22.3%
10010100 17345
 
10.4%
10020500 2243
 
1.4%
10030300 113
 
0.1%
10030200 97
 
0.1%
10010400 54
 
< 0.1%
10030100 2
 
< 0.1%
ValueCountFrequency (%)
10010100 17345
 
10.4%
10010200 47657
28.7%
10010400 54
 
< 0.1%
10020000 36989
22.3%
10020500 2243
 
1.4%
10030100 2
 
< 0.1%
10030200 97
 
0.1%
10030300 113
 
0.1%
10040000 61543
37.1%
ValueCountFrequency (%)
10040000 61543
37.1%
10030300 113
 
0.1%
10030200 97
 
0.1%
10030100 2
 
< 0.1%
10020500 2243
 
1.4%
10020000 36989
22.3%
10010400 54
 
< 0.1%
10010200 47657
28.7%
10010100 17345
 
10.4%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.6 MiB
Recursos de outros candidatos
61543 
Recursos de pessoas físicas
47657 
Recursos de partido político
36989 
Recursos próprios
17345 
Recursos de Financiamento Coletivo
 
2243
Other values (4)
 
266

Length

Max length48
Median length37
Mean length27.024957
Min length17

Characters and Unicode

Total characters4487305
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRecursos de pessoas físicas
2nd rowRecursos de pessoas físicas
3rd rowRecursos de pessoas físicas
4th rowRecursos de pessoas físicas
5th rowRecursos de pessoas físicas

Common Values

ValueCountFrequency (%)
Recursos de outros candidatos 61543
37.1%
Recursos de pessoas físicas 47657
28.7%
Recursos de partido político 36989
22.3%
Recursos próprios 17345
 
10.4%
Recursos de Financiamento Coletivo 2243
 
1.4%
Recursos de origens não identificadas 113
 
0.1%
Rendimentos de aplicações financeiras 97
 
0.1%
Doações pela Internet 54
 
< 0.1%
Comercialização de bens ou realização de eventos 2
 
< 0.1%

Length

2022-12-14T11:38:23.364933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:23.494934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
recursos 165890
26.4%
de 148646
23.6%
outros 61543
 
9.8%
candidatos 61543
 
9.8%
pessoas 47657
 
7.6%
físicas 47657
 
7.6%
partido 36989
 
5.9%
político 36989
 
5.9%
próprios 17345
 
2.8%
financiamento 2243
 
0.4%
Other values (15) 3045
 
0.5%

Most occurring characters

ValueCountFrequency (%)
s 711071
15.8%
o 533604
11.9%
463504
10.3%
e 367519
8.2%
c 314631
 
7.0%
d 309044
 
6.9%
r 299380
 
6.7%
a 260605
 
5.8%
u 227435
 
5.1%
i 208098
 
4.6%
Other values (20) 792414
17.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3853218
85.9%
Space Separator 463504
 
10.3%
Uppercase Letter 170583
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 711071
18.5%
o 533604
13.8%
e 367519
9.5%
c 314631
8.2%
d 309044
8.0%
r 299380
7.8%
a 260605
 
6.8%
u 227435
 
5.9%
i 208098
 
5.4%
t 201870
 
5.2%
Other values (14) 419961
10.9%
Uppercase Letter
ValueCountFrequency (%)
R 165987
97.3%
C 2245
 
1.3%
F 2243
 
1.3%
D 54
 
< 0.1%
I 54
 
< 0.1%
Space Separator
ValueCountFrequency (%)
463504
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4023801
89.7%
Common 463504
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 711071
17.7%
o 533604
13.3%
e 367519
9.1%
c 314631
7.8%
d 309044
7.7%
r 299380
7.4%
a 260605
 
6.5%
u 227435
 
5.7%
i 208098
 
5.2%
t 201870
 
5.0%
Other values (19) 590544
14.7%
Common
ValueCountFrequency (%)
463504
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4384891
97.7%
None 102414
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 711071
16.2%
o 533604
12.2%
463504
10.6%
e 367519
8.4%
c 314631
7.2%
d 309044
7.0%
r 299380
6.8%
a 260605
 
5.9%
u 227435
 
5.2%
i 208098
 
4.7%
Other values (15) 690000
15.7%
None
ValueCountFrequency (%)
í 84646
82.7%
ó 17345
 
16.9%
ç 155
 
0.2%
õ 151
 
0.1%
ã 117
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
0
104426 
1
61617 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 104426
62.9%
1 61617
37.1%

Length

2022-12-14T11:38:23.627922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:23.730848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 104426
62.9%
1 61617
37.1%

Most occurring characters

ValueCountFrequency (%)
0 104426
62.9%
1 61617
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166043
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 104426
62.9%
1 61617
37.1%

Most occurring scripts

ValueCountFrequency (%)
Common 166043
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 104426
62.9%
1 61617
37.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 104426
62.9%
1 61617
37.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
ESTIMÁVEL
104426 
FINANCEIRO
61617 

Length

Max length10
Median length9
Mean length9.3710906
Min length9

Characters and Unicode

Total characters1556004
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFINANCEIRO
2nd rowFINANCEIRO
3rd rowFINANCEIRO
4th rowFINANCEIRO
5th rowFINANCEIRO

Common Values

ValueCountFrequency (%)
ESTIMÁVEL 104426
62.9%
FINANCEIRO 61617
37.1%

Length

2022-12-14T11:38:23.813842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:23.917842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
estimável 104426
62.9%
financeiro 61617
37.1%

Most occurring characters

ValueCountFrequency (%)
E 270469
17.4%
I 227660
14.6%
N 123234
7.9%
S 104426
 
6.7%
T 104426
 
6.7%
M 104426
 
6.7%
Á 104426
 
6.7%
V 104426
 
6.7%
L 104426
 
6.7%
F 61617
 
4.0%
Other values (4) 246468
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1556004
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 270469
17.4%
I 227660
14.6%
N 123234
7.9%
S 104426
 
6.7%
T 104426
 
6.7%
M 104426
 
6.7%
Á 104426
 
6.7%
V 104426
 
6.7%
L 104426
 
6.7%
F 61617
 
4.0%
Other values (4) 246468
15.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1556004
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 270469
17.4%
I 227660
14.6%
N 123234
7.9%
S 104426
 
6.7%
T 104426
 
6.7%
M 104426
 
6.7%
Á 104426
 
6.7%
V 104426
 
6.7%
L 104426
 
6.7%
F 61617
 
4.0%
Other values (4) 246468
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1451578
93.3%
None 104426
 
6.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 270469
18.6%
I 227660
15.7%
N 123234
8.5%
S 104426
 
7.2%
T 104426
 
7.2%
M 104426
 
7.2%
V 104426
 
7.2%
L 104426
 
7.2%
F 61617
 
4.2%
A 61617
 
4.2%
Other values (3) 184851
12.7%
None
ValueCountFrequency (%)
Á 104426
100.0%

CD_ESPECIE_RECEITA
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2401848
Minimum0
Maximum19
Zeros1357
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:23.995839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.5206298
Coefficient of variation (CV)1.1251883
Kurtosis35.633802
Mean2.2401848
Median Absolute Deviation (MAD)0
Skewness5.8236455
Sum371967
Variance6.3535743
MonotonicityNot monotonic
2022-12-14T11:38:24.076849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 104426
62.9%
1 42783
25.8%
4 13651
 
8.2%
19 3230
 
1.9%
0 1357
 
0.8%
5 185
 
0.1%
3 136
 
0.1%
9 109
 
0.1%
16 97
 
0.1%
6 33
 
< 0.1%
Other values (2) 36
 
< 0.1%
ValueCountFrequency (%)
0 1357
 
0.8%
1 42783
25.8%
2 104426
62.9%
3 136
 
0.1%
4 13651
 
8.2%
5 185
 
0.1%
6 33
 
< 0.1%
7 29
 
< 0.1%
9 109
 
0.1%
13 7
 
< 0.1%
ValueCountFrequency (%)
19 3230
 
1.9%
16 97
 
0.1%
13 7
 
< 0.1%
9 109
 
0.1%
7 29
 
< 0.1%
6 33
 
< 0.1%
5 185
 
0.1%
4 13651
 
8.2%
3 136
 
0.1%
2 104426
62.9%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.4 MiB
Estimado
104426 
Transferência eletrônica
42783 
Depósito em espécie
13651 
PIX
 
3230
Cheque
 
1357
Other values (7)
 
596

Length

Max length25
Median length8
Mean length12.933162
Min length2

Characters and Unicode

Total characters2147461
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransferência eletrônica
2nd rowPIX
3rd rowPIX
4th rowPIX
5th rowPIX

Common Values

ValueCountFrequency (%)
Estimado 104426
62.9%
Transferência eletrônica 42783
25.8%
Depósito em espécie 13651
 
8.2%
PIX 3230
 
1.9%
Cheque 1357
 
0.8%
Em espécie 185
 
0.1%
Outros títulos de crédito 136
 
0.1%
Não informado 109
 
0.1%
-- 97
 
0.1%
Boleto de cobrança 33
 
< 0.1%
Other values (2) 36
 
< 0.1%

Length

2022-12-14T11:38:24.179845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
estimado 104426
44.1%
eletrônica 42783
18.1%
transferência 42783
18.1%
em 13836
 
5.8%
espécie 13836
 
5.8%
depósito 13651
 
5.8%
pix 3230
 
1.4%
cheque 1357
 
0.6%
de 205
 
0.1%
crédito 165
 
0.1%
Other values (9) 696
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 232986
10.8%
i 217760
 
10.1%
e 186275
 
8.7%
s 174968
 
8.1%
t 161509
 
7.5%
r 128828
 
6.0%
n 128491
 
6.0%
o 118983
 
5.5%
m 118371
 
5.5%
d 104912
 
4.9%
Other values (27) 574378
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1903936
88.7%
Uppercase Letter 172406
 
8.0%
Space Separator 70925
 
3.3%
Dash Punctuation 194
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 232986
12.2%
i 217760
11.4%
e 186275
9.8%
s 174968
9.2%
t 161509
8.5%
r 128828
 
6.8%
n 128491
 
6.7%
o 118983
 
6.2%
m 118371
 
6.2%
d 104912
 
5.5%
Other values (15) 330853
17.4%
Uppercase Letter
ValueCountFrequency (%)
E 104611
60.7%
T 42783
24.8%
D 13651
 
7.9%
P 3230
 
1.9%
I 3230
 
1.9%
X 3230
 
1.9%
C 1393
 
0.8%
O 136
 
0.1%
N 109
 
0.1%
B 33
 
< 0.1%
Space Separator
ValueCountFrequency (%)
70925
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2076342
96.7%
Common 71119
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 232986
11.2%
i 217760
10.5%
e 186275
 
9.0%
s 174968
 
8.4%
t 161509
 
7.8%
r 128828
 
6.2%
n 128491
 
6.2%
o 118983
 
5.7%
m 118371
 
5.7%
d 104912
 
5.1%
Other values (25) 503259
24.2%
Common
ValueCountFrequency (%)
70925
99.7%
- 194
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2033922
94.7%
None 113539
 
5.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 232986
11.5%
i 217760
10.7%
e 186275
9.2%
s 174968
 
8.6%
t 161509
 
7.9%
r 128828
 
6.3%
n 128491
 
6.3%
o 118983
 
5.8%
m 118371
 
5.8%
d 104912
 
5.2%
Other values (20) 460839
22.7%
None
ValueCountFrequency (%)
ê 42783
37.7%
ô 42783
37.7%
é 14008
 
12.3%
ó 13651
 
12.0%
ã 145
 
0.1%
í 136
 
0.1%
ç 33
 
< 0.1%

CD_CNAE_DOADOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct31
Distinct (%)< 0.1%
Missing65274
Missing (%)39.3%
Infinite0
Infinite (%)0.0%
Mean94296.698
Minimum17311
Maximum94995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:24.277838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum17311
5-th percentile94928
Q194928
median94928
Q394928
95-th percentile94928
Maximum94995
Range77684
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4407.4934
Coefficient of variation (CV)0.046740698
Kurtosis98.670738
Mean94296.698
Median Absolute Deviation (MAD)0
Skewness-8.7589557
Sum9.5021839 × 109
Variance19425998
MonotonicityNot monotonic
2022-12-14T11:38:24.374839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
94928 98170
59.1%
74901 1314
 
0.8%
63119 308
 
0.2%
94308 187
 
0.1%
62091 174
 
0.1%
62015 161
 
0.1%
62023 142
 
0.1%
18130 50
 
< 0.1%
63194 44
 
< 0.1%
69206 43
 
< 0.1%
Other values (21) 176
 
0.1%
(Missing) 65274
39.3%
ValueCountFrequency (%)
17311 1
 
< 0.1%
18113 19
 
< 0.1%
18130 50
< 0.1%
18211 9
 
< 0.1%
32990 4
 
< 0.1%
47318 2
 
< 0.1%
47512 2
 
< 0.1%
58115 22
< 0.1%
58191 1
 
< 0.1%
58221 3
 
< 0.1%
ValueCountFrequency (%)
94995 7
 
< 0.1%
94928 98170
59.1%
94308 187
 
0.1%
94201 9
 
< 0.1%
93123 28
 
< 0.1%
85121 1
 
< 0.1%
82997 10
 
< 0.1%
82300 1
 
< 0.1%
74901 1314
 
0.8%
74200 1
 
< 0.1%

DS_CNAE_DOADOR
Categorical

HIGH CORRELATION
MISSING

Distinct31
Distinct (%)< 0.1%
Missing65274
Missing (%)39.3%
Memory size13.8 MiB
Atividades de organizações políticas
98170 
Atividades profissionais, científicas e técnicas não especificadas anteriormente
 
1314
Tratamento de dados, provedores de serviços de aplicação e serviços de hospedagem na Internet
 
308
Atividades de associações de defesa de direitos sociais
 
187
Suporte técnico, manutenção e outros serviços em tecnologia da informação
 
174
Other values (26)
 
616

Length

Max length93
Median length36
Mean length36.985372
Min length16

Characters and Unicode

Total characters3726979
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowAtividades de organizações políticas
2nd rowAtividades de organizações políticas
3rd rowAtividades de organizações políticas
4th rowAtividades de organizações políticas
5th rowAtividades de organizações políticas

Common Values

ValueCountFrequency (%)
Atividades de organizações políticas 98170
59.1%
Atividades profissionais, científicas e técnicas não especificadas anteriormente 1314
 
0.8%
Tratamento de dados, provedores de serviços de aplicação e serviços de hospedagem na Internet 308
 
0.2%
Atividades de associações de defesa de direitos sociais 187
 
0.1%
Suporte técnico, manutenção e outros serviços em tecnologia da informação 174
 
0.1%
Desenvolvimento de programas de computador sob encomenda 161
 
0.1%
Desenvolvimento e licenciamento de programas de computador customizáveis 142
 
0.1%
Impressão de materiais para outros usos 50
 
< 0.1%
Portais, provedores de conteúdo e outros serviços de informação na Internet 44
 
< 0.1%
Atividades de contabilidade, consultoria e auditoria contábil e tributária 43
 
< 0.1%
Other values (21) 176
 
0.1%
(Missing) 65274
39.3%

Length

2022-12-14T11:38:24.496850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 100894
24.3%
atividades 99778
24.0%
organizações 98179
23.6%
políticas 98170
23.6%
e 2131
 
0.5%
não 1346
 
0.3%
anteriormente 1346
 
0.3%
especificadas 1342
 
0.3%
profissionais 1314
 
0.3%
científicas 1314
 
0.3%
Other values (96) 9417
 
2.3%

Most occurring characters

ValueCountFrequency (%)
i 412453
11.1%
a 408123
 
11.0%
e 315918
 
8.5%
314462
 
8.4%
s 311778
 
8.4%
d 304410
 
8.2%
o 209908
 
5.6%
t 207236
 
5.6%
n 109954
 
3.0%
c 108581
 
2.9%
Other values (35) 1024156
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3309423
88.8%
Space Separator 314462
 
8.4%
Uppercase Letter 101121
 
2.7%
Other Punctuation 1962
 
0.1%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 412453
12.5%
a 408123
12.3%
e 315918
 
9.5%
s 311778
 
9.4%
d 304410
 
9.2%
o 209908
 
6.3%
t 207236
 
6.3%
n 109954
 
3.3%
c 108581
 
3.3%
r 106966
 
3.2%
Other values (23) 814096
24.6%
Uppercase Letter
ValueCountFrequency (%)
A 99791
98.7%
I 421
 
0.4%
T 308
 
0.3%
D 303
 
0.3%
S 183
 
0.2%
P 44
 
< 0.1%
E 34
 
< 0.1%
C 32
 
< 0.1%
F 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
314462
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1962
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3410544
91.5%
Common 316435
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 412453
12.1%
a 408123
12.0%
e 315918
 
9.3%
s 311778
 
9.1%
d 304410
 
8.9%
o 209908
 
6.2%
t 207236
 
6.1%
n 109954
 
3.2%
c 108581
 
3.2%
r 106966
 
3.1%
Other values (32) 915217
26.8%
Common
ValueCountFrequency (%)
314462
99.4%
, 1962
 
0.6%
- 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3425065
91.9%
None 301914
 
8.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 412453
12.0%
a 408123
11.9%
e 315918
9.2%
314462
9.2%
s 311778
9.1%
d 304410
8.9%
o 209908
 
6.1%
t 207236
 
6.1%
n 109954
 
3.2%
c 108581
 
3.2%
Other values (25) 722242
21.1%
None
ValueCountFrequency (%)
ç 99993
33.1%
í 99492
33.0%
õ 98385
32.6%
ã 2203
 
0.7%
é 1502
 
0.5%
á 243
 
0.1%
ú 44
 
< 0.1%
à 20
 
< 0.1%
ó 19
 
< 0.1%
ê 13
 
< 0.1%

NR_CPF_CNPJ_DOADOR
Real number (ℝ)

Distinct43817
Distinct (%)26.4%
Missing210
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.7282522 × 1013
Minimum0
Maximum7.4011958 × 1013
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:24.615846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6829489 × 109
Q11.6098822 × 1010
median8.943332 × 1012
Q33.8607109 × 1013
95-th percentile3.9127067 × 1013
Maximum7.4011958 × 1013
Range7.4011958 × 1013
Interquartile range (IQR)3.859101 × 1013

Descriptive statistics

Standard deviation1.7541135 × 1013
Coefficient of variation (CV)1.0149638
Kurtosis-1.6739345
Mean1.7282522 × 1013
Median Absolute Deviation (MAD)8.9402028 × 1012
Skewness0.27847389
Sum2.8660124 × 1018
Variance3.076914 × 1026
MonotonicityNot monotonic
2022-12-14T11:38:24.731842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38957760000164.0 2158
 
1.3%
7959911000190.0 1934
 
1.2%
31192176000116.0 1828
 
1.1%
38757309000101.0 1742
 
1.0%
31208533000197.0 1248
 
0.8%
3518656000199.0 1166
 
0.7%
39127067000127.0 1153
 
0.7%
8049726000120.0 1133
 
0.7%
2144766000175.0 1024
 
0.6%
21214898000170.0 970
 
0.6%
Other values (43807) 151477
91.2%
ValueCountFrequency (%)
0.0 2
< 0.1%
1.0 1
< 0.1%
2.0 1
< 0.1%
5.0 1
< 0.1%
6.0 1
< 0.1%
7.0 1
< 0.1%
9.0 1
< 0.1%
10.0 1
< 0.1%
1277790.0 1
< 0.1%
2447754.0 1
< 0.1%
ValueCountFrequency (%)
74011958000114.0 1
 
< 0.1%
73282907000245.0 38
 
< 0.1%
73282907000164.0 10
 
< 0.1%
59933952000100.0 248
0.1%
54956495000156.0 357
0.2%
47744630000134.0 319
0.2%
47582560000165.0 12
 
< 0.1%
47582513000111.0 1
 
< 0.1%
47582503000186.0 1
 
< 0.1%
47582496000112.0 1
 
< 0.1%

NM_DOADOR
Categorical

Distinct47664
Distinct (%)28.7%
Missing210
Missing (%)0.1%
Memory size15.4 MiB
Direção Municipal/Comissão Provisória
16728 
Direção Estadual/Distrital
14382 
Direção Nacional
 
5524
ELEIÇÃO 2020 WAGNER DOS SANTOS CARNEIRO PREFEITO
 
1261
JORGE LUCIO FERREIRA MIRANDA
 
741
Other values (47659)
127197 

Length

Max length150
Median length70
Mean length30.40261
Min length1

Characters and Unicode

Total characters5041756
Distinct characters89
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35235 ?
Unique (%)21.2%

Sample

1st rowANDRE LUIZ ISSA VIEIRA
2nd rowALEX DE PAULA OLIVEIRA
3rd rowUBIRAJARA PIMENTEL CANDIDO
4th rowCRISTIANO DE FREITAS NEVES
5th rowFERNANDO NASCENTES PIRES

Common Values

ValueCountFrequency (%)
Direção Municipal/Comissão Provisória 16728
 
10.1%
Direção Estadual/Distrital 14382
 
8.7%
Direção Nacional 5524
 
3.3%
ELEIÇÃO 2020 WAGNER DOS SANTOS CARNEIRO PREFEITO 1261
 
0.8%
JORGE LUCIO FERREIRA MIRANDA 741
 
0.4%
ELEICAO 2020 ANTONIO FRANCISCO NETO 721
 
0.4%
ELEICAO 2020 RAFAEL PAES BARBOSA DINIZ NOGUEIRA PREFEITO 692
 
0.4%
ELEIÇÃO 2018 WILSON JOSE WITZEL GOVERNADOR 584
 
0.4%
JOSE BONIFACIO FERREIRA NOVELLINO 564
 
0.3%
LUIZ ANTONIO DANTAS RIBEIRO 532
 
0.3%
Other values (47654) 124104
74.7%

Length

2022-12-14T11:38:24.876137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
direção 36983
 
5.4%
de 28682
 
4.2%
2020 25588
 
3.7%
da 20356
 
3.0%
prefeito 18747
 
2.7%
eleição 17643
 
2.6%
provisória 16802
 
2.5%
municipal/comissão 16802
 
2.5%
silva 16285
 
2.4%
estadual/distrital 14425
 
2.1%
Other values (16154) 473372
69.0%

Most occurring characters

ValueCountFrequency (%)
535773
 
10.6%
E 433425
 
8.6%
A 427487
 
8.5%
O 329747
 
6.5%
I 309520
 
6.1%
R 285088
 
5.7%
D 209649
 
4.2%
S 204405
 
4.1%
L 197122
 
3.9%
N 163525
 
3.2%
Other values (79) 1946015
38.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3434956
68.1%
Lowercase Letter 891750
 
17.7%
Space Separator 535773
 
10.6%
Decimal Number 143645
 
2.8%
Other Punctuation 34113
 
0.7%
Dash Punctuation 1496
 
< 0.1%
Close Punctuation 11
 
< 0.1%
Open Punctuation 10
 
< 0.1%
Currency Symbol 1
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 433425
12.6%
A 427487
12.4%
O 329747
9.6%
I 309520
9.0%
R 285088
 
8.3%
D 209649
 
6.1%
S 204405
 
6.0%
L 197122
 
5.7%
N 163525
 
4.8%
C 119539
 
3.5%
Other values (27) 755449
22.0%
Lowercase Letter
ValueCountFrequency (%)
i 156262
17.5%
o 94566
10.6%
a 89624
10.1%
r 85454
9.6%
s 79418
8.9%
ã 53362
 
6.0%
l 51773
 
5.8%
t 43854
 
4.9%
e 38668
 
4.3%
ç 36652
 
4.1%
Other values (20) 162117
18.2%
Decimal Number
ValueCountFrequency (%)
2 64328
44.8%
0 62468
43.5%
1 8273
 
5.8%
8 8251
 
5.7%
3 70
 
< 0.1%
7 66
 
< 0.1%
5 65
 
< 0.1%
9 52
 
< 0.1%
4 41
 
< 0.1%
6 31
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 31261
91.6%
. 2807
 
8.2%
· 32
 
0.1%
? 12
 
< 0.1%
: 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 10
90.9%
] 1
 
9.1%
Space Separator
ValueCountFrequency (%)
535773
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1496
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 1
100.0%
Other Symbol
ValueCountFrequency (%)
© 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4326706
85.8%
Common 715050
 
14.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 433425
 
10.0%
A 427487
 
9.9%
O 329747
 
7.6%
I 309520
 
7.2%
R 285088
 
6.6%
D 209649
 
4.8%
S 204405
 
4.7%
L 197122
 
4.6%
N 163525
 
3.8%
i 156262
 
3.6%
Other values (57) 1610476
37.2%
Common
ValueCountFrequency (%)
535773
74.9%
2 64328
 
9.0%
0 62468
 
8.7%
/ 31261
 
4.4%
1 8273
 
1.2%
8 8251
 
1.2%
. 2807
 
0.4%
- 1496
 
0.2%
3 70
 
< 0.1%
7 66
 
< 0.1%
Other values (12) 257
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4875466
96.7%
None 166290
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
535773
 
11.0%
E 433425
 
8.9%
A 427487
 
8.8%
O 329747
 
6.8%
I 309520
 
6.3%
R 285088
 
5.8%
D 209649
 
4.3%
S 204405
 
4.2%
L 197122
 
4.0%
N 163525
 
3.4%
Other values (61) 1779725
36.5%
None
ValueCountFrequency (%)
ã 53362
32.1%
ç 36652
22.0%
Ç 26294
15.8%
à 22841
13.7%
ó 16728
 
10.1%
Õ 3478
 
2.1%
É 3335
 
2.0%
Á 879
 
0.5%
Ô 715
 
0.4%
Í 662
 
0.4%
Other values (8) 1344
 
0.8%

NM_DOADOR_RFB
Categorical

Distinct43540
Distinct (%)26.3%
Missing246
Missing (%)0.1%
Memory size15.2 MiB
ELEICAO 2020 ALEXANDRE AUGUSTUS SERFIOTIS PREFEITO
 
2158
REPUBLICANOS - RIO DE JANEIRO - RJ - ESTADUAL
 
1934
ELEICAO 2018 EDUARDO DA COSTA PAES GOVERNADOR
 
1828
ELEICAO 2020 WAGNER DOS SANTOS CARNEIRO PREFEITO
 
1742
ELEICAO 2018 WILSON JOSE WITZEL GOVERNADOR
 
1248
Other values (43535)
156887 

Length

Max length95
Median length74
Mean length39.20232
Min length6

Characters and Unicode

Total characters6499627
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32057 ?
Unique (%)19.3%

Sample

1st rowANDRE LUIZ ISSA VIEIRA
2nd rowALEX DE PAULA OLIVEIRA
3rd rowUBIRAJARA PIMENTEL CANDIDO
4th rowCRISTIANO DE FREITAS NEVES
5th rowFERNANDO NASCENTES PIRES

Common Values

ValueCountFrequency (%)
ELEICAO 2020 ALEXANDRE AUGUSTUS SERFIOTIS PREFEITO 2158
 
1.3%
REPUBLICANOS - RIO DE JANEIRO - RJ - ESTADUAL 1934
 
1.2%
ELEICAO 2018 EDUARDO DA COSTA PAES GOVERNADOR 1828
 
1.1%
ELEICAO 2020 WAGNER DOS SANTOS CARNEIRO PREFEITO 1742
 
1.0%
ELEICAO 2018 WILSON JOSE WITZEL GOVERNADOR 1248
 
0.8%
ELEICAO 2020 RAFAEL PAES BARBOSA DINIZ NOGUEIRA PREFEITO 1153
 
0.7%
DEMOCRACIA CRISTA - RIO DE JANEIRO - RJ - ESTADUAL 1024
 
0.6%
PARTIDO SOCIALISMO E LIBERDADE - RIO DE JANEIRO - RJ - ESTADUAL 969
 
0.6%
CIDADANIA - 23 - ESTADUAL - RJ 949
 
0.6%
PARTIDO TRABALHISTA BRASILEIRO RIO DE JANEIRO RJ ESTADUAL 941
 
0.6%
Other values (43530) 151851
91.5%

Length

2022-12-14T11:38:25.014876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
eleicao 61647
 
6.3%
2020 47108
 
4.8%
46124
 
4.7%
prefeito 45758
 
4.7%
de 44379
 
4.6%
da 24941
 
2.6%
partido 23540
 
2.4%
silva 16904
 
1.7%
rj 14978
 
1.5%
do 14426
 
1.5%
Other values (14421) 631499
65.0%

Most occurring characters

ValueCountFrequency (%)
808347
12.4%
A 702413
10.8%
E 653670
10.1%
O 572279
 
8.8%
I 552389
 
8.5%
R 489589
 
7.5%
L 303958
 
4.7%
S 297872
 
4.6%
D 288977
 
4.4%
T 238866
 
3.7%
Other values (33) 1591267
24.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5385192
82.9%
Space Separator 808347
 
12.4%
Decimal Number 249839
 
3.8%
Dash Punctuation 53517
 
0.8%
Other Punctuation 1248
 
< 0.1%
Open Punctuation 742
 
< 0.1%
Close Punctuation 742
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 702413
13.0%
E 653670
12.1%
O 572279
10.6%
I 552389
10.3%
R 489589
9.1%
L 303958
 
5.6%
S 297872
 
5.5%
D 288977
 
5.4%
T 238866
 
4.4%
C 220955
 
4.1%
Other values (16) 1064224
19.8%
Decimal Number
ValueCountFrequency (%)
2 113199
45.3%
0 109092
43.7%
1 13264
 
5.3%
8 12966
 
5.2%
3 958
 
0.4%
5 325
 
0.1%
4 13
 
< 0.1%
7 13
 
< 0.1%
6 7
 
< 0.1%
9 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 634
50.8%
. 602
48.2%
, 12
 
1.0%
Space Separator
ValueCountFrequency (%)
808347
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53517
100.0%
Open Punctuation
ValueCountFrequency (%)
( 742
100.0%
Close Punctuation
ValueCountFrequency (%)
) 742
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5385192
82.9%
Common 1114435
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 702413
13.0%
E 653670
12.1%
O 572279
10.6%
I 552389
10.3%
R 489589
9.1%
L 303958
 
5.6%
S 297872
 
5.5%
D 288977
 
5.4%
T 238866
 
4.4%
C 220955
 
4.1%
Other values (16) 1064224
19.8%
Common
ValueCountFrequency (%)
808347
72.5%
2 113199
 
10.2%
0 109092
 
9.8%
- 53517
 
4.8%
1 13264
 
1.2%
8 12966
 
1.2%
3 958
 
0.1%
( 742
 
0.1%
) 742
 
0.1%
/ 634
 
0.1%
Other values (7) 974
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6499627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
808347
12.4%
A 702413
10.8%
E 653670
10.1%
O 572279
 
8.8%
I 552389
 
8.5%
R 489589
 
7.5%
L 303958
 
4.7%
S 297872
 
4.6%
D 288977
 
4.4%
T 238866
 
3.7%
Other values (33) 1591267
24.5%

CD_ESFERA_PARTIDARIA_DOADOR
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing129053
Missing (%)77.7%
Memory size7.0 MiB
M
16802 
F
14430 
N
5758 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36990
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 16802
 
10.1%
F 14430
 
8.7%
N 5758
 
3.5%
(Missing) 129053
77.7%

Length

2022-12-14T11:38:25.113877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:25.211877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
m 16802
45.4%
f 14430
39.0%
n 5758
 
15.6%

Most occurring characters

ValueCountFrequency (%)
M 16802
45.4%
F 14430
39.0%
N 5758
 
15.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 36990
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 16802
45.4%
F 14430
39.0%
N 5758
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 36990
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 16802
45.4%
F 14430
39.0%
N 5758
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 16802
45.4%
F 14430
39.0%
N 5758
 
15.6%

DS_ESFERA_PARTIDARIA_DOADOR
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing129053
Missing (%)77.7%
Memory size7.5 MiB
Municipal
16802 
Federal (Estadual/Distrital)
14430 
Nacional
5758 

Length

Max length28
Median length9
Mean length16.25634
Min length8

Characters and Unicode

Total characters601322
Distinct characters22
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNacional
2nd rowFederal (Estadual/Distrital)
3rd rowFederal (Estadual/Distrital)
4th rowFederal (Estadual/Distrital)
5th rowFederal (Estadual/Distrital)

Common Values

ValueCountFrequency (%)
Municipal 16802
 
10.1%
Federal (Estadual/Distrital) 14430
 
8.7%
Nacional 5758
 
3.5%
(Missing) 129053
77.7%

Length

2022-12-14T11:38:25.300872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:25.409886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
municipal 16802
32.7%
federal 14430
28.1%
estadual/distrital 14430
28.1%
nacional 5758
 
11.2%

Most occurring characters

ValueCountFrequency (%)
a 86038
14.3%
i 68222
11.3%
l 65850
 
11.0%
t 43290
 
7.2%
u 31232
 
5.2%
r 28860
 
4.8%
s 28860
 
4.8%
e 28860
 
4.8%
d 28860
 
4.8%
n 22560
 
3.8%
Other values (12) 168690
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 477752
79.5%
Uppercase Letter 65850
 
11.0%
Space Separator 14430
 
2.4%
Open Punctuation 14430
 
2.4%
Other Punctuation 14430
 
2.4%
Close Punctuation 14430
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 86038
18.0%
i 68222
14.3%
l 65850
13.8%
t 43290
9.1%
u 31232
 
6.5%
r 28860
 
6.0%
s 28860
 
6.0%
e 28860
 
6.0%
d 28860
 
6.0%
n 22560
 
4.7%
Other values (3) 45120
9.4%
Uppercase Letter
ValueCountFrequency (%)
M 16802
25.5%
E 14430
21.9%
F 14430
21.9%
D 14430
21.9%
N 5758
 
8.7%
Space Separator
ValueCountFrequency (%)
14430
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14430
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 14430
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 543602
90.4%
Common 57720
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 86038
15.8%
i 68222
12.5%
l 65850
12.1%
t 43290
 
8.0%
u 31232
 
5.7%
r 28860
 
5.3%
s 28860
 
5.3%
e 28860
 
5.3%
d 28860
 
5.3%
n 22560
 
4.2%
Other values (8) 110970
20.4%
Common
ValueCountFrequency (%)
14430
25.0%
( 14430
25.0%
/ 14430
25.0%
) 14430
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 601322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 86038
14.3%
i 68222
11.3%
l 65850
 
11.0%
t 43290
 
7.2%
u 31232
 
5.2%
r 28860
 
4.8%
s 28860
 
4.8%
e 28860
 
4.8%
d 28860
 
4.8%
n 22560
 
3.8%
Other values (12) 168690
28.1%

SG_UF_DOADOR
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)< 0.1%
Missing50163
Missing (%)30.2%
Memory size8.4 MiB
RJ
110080 
BR
 
5763
MG
 
11
MA
 
8
PI
 
7
Other values (5)
 
11

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters231760
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRJ
2nd rowRJ
3rd rowRJ
4th rowRJ
5th rowRJ

Common Values

ValueCountFrequency (%)
RJ 110080
66.3%
BR 5763
 
3.5%
MG 11
 
< 0.1%
MA 8
 
< 0.1%
PI 7
 
< 0.1%
DF 4
 
< 0.1%
SP 2
 
< 0.1%
PR 2
 
< 0.1%
ES 2
 
< 0.1%
MT 1
 
< 0.1%
(Missing) 50163
30.2%

Length

2022-12-14T11:38:25.498116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-14T11:38:25.802116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
rj 110080
95.0%
br 5763
 
5.0%
mg 11
 
< 0.1%
ma 8
 
< 0.1%
pi 7
 
< 0.1%
df 4
 
< 0.1%
sp 2
 
< 0.1%
pr 2
 
< 0.1%
es 2
 
< 0.1%
mt 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 115845
50.0%
J 110080
47.5%
B 5763
 
2.5%
M 20
 
< 0.1%
G 11
 
< 0.1%
P 11
 
< 0.1%
A 8
 
< 0.1%
I 7
 
< 0.1%
D 4
 
< 0.1%
F 4
 
< 0.1%
Other values (3) 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 231760
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 115845
50.0%
J 110080
47.5%
B 5763
 
2.5%
M 20
 
< 0.1%
G 11
 
< 0.1%
P 11
 
< 0.1%
A 8
 
< 0.1%
I 7
 
< 0.1%
D 4
 
< 0.1%
F 4
 
< 0.1%
Other values (3) 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 231760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 115845
50.0%
J 110080
47.5%
B 5763
 
2.5%
M 20
 
< 0.1%
G 11
 
< 0.1%
P 11
 
< 0.1%
A 8
 
< 0.1%
I 7
 
< 0.1%
D 4
 
< 0.1%
F 4
 
< 0.1%
Other values (3) 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 115845
50.0%
J 110080
47.5%
B 5763
 
2.5%
M 20
 
< 0.1%
G 11
 
< 0.1%
P 11
 
< 0.1%
A 8
 
< 0.1%
I 7
 
< 0.1%
D 4
 
< 0.1%
F 4
 
< 0.1%
Other values (3) 7
 
< 0.1%

CD_MUNICIPIO_DOADOR
Real number (ℝ)

HIGH CORRELATION
MISSING
SKEWED

Distinct95
Distinct (%)0.1%
Missing89465
Missing (%)53.9%
Infinite0
Infinite (%)0.0%
Mean58638.479
Minimum8257
Maximum97012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:25.917116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum8257
5-th percentile58041
Q158190
median58491
Q358971
95-th percentile60011
Maximum97012
Range88755
Interquartile range (IQR)781

Descriptive statistics

Standard deviation792.57314
Coefficient of variation (CV)0.013516264
Kurtosis1809.2458
Mean58638.479
Median Absolute Deviation (MAD)340
Skewness-26.439158
Sum4.4904174 × 109
Variance628172.18
MonotonicityNot monotonic
2022-12-14T11:38:26.029108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60011 6136
 
3.7%
58190 5203
 
3.1%
58076 2989
 
1.8%
58327 2877
 
1.7%
58041 2541
 
1.5%
58653 2324
 
1.4%
59250 2186
 
1.3%
58670 1938
 
1.2%
58971 1855
 
1.1%
59153 1786
 
1.1%
Other values (85) 46743
28.2%
(Missing) 89465
53.9%
ValueCountFrequency (%)
8257 8
 
< 0.1%
40290 9
 
< 0.1%
58009 910
 
0.5%
58017 905
 
0.5%
58025 487
 
0.3%
58033 1333
0.8%
58041 2541
1.5%
58050 736
 
0.4%
58068 593
 
0.4%
58076 2989
1.8%
ValueCountFrequency (%)
97012 1
 
< 0.1%
60011 6136
3.7%
59331 180
 
0.1%
59315 651
 
0.4%
59293 340
 
0.2%
59277 315
 
0.2%
59250 2186
 
1.3%
59234 578
 
0.3%
59218 596
 
0.4%
59196 1480
 
0.9%

NM_MUNICIPIO_DOADOR
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct95
Distinct (%)0.1%
Missing89465
Missing (%)53.9%
Memory size9.0 MiB
RIO DE JANEIRO
6136 
CAMPOS DOS GOYTACAZES
 
5203
BARRA MANSA
 
2989
PORTO REAL
 
2877
BELFORD ROXO
 
2541
Other values (90)
56832 

Length

Max length29
Median length22
Mean length11.934603
Min length4

Characters and Unicode

Total characters913928
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRIO DE JANEIRO
2nd rowRIO DE JANEIRO
3rd rowRIO DE JANEIRO
4th rowRIO DE JANEIRO
5th rowRIO DE JANEIRO

Common Values

ValueCountFrequency (%)
RIO DE JANEIRO 6136
 
3.7%
CAMPOS DOS GOYTACAZES 5203
 
3.1%
BARRA MANSA 2989
 
1.8%
PORTO REAL 2877
 
1.7%
BELFORD ROXO 2541
 
1.5%
NITERÓI 2324
 
1.4%
VOLTA REDONDA 2186
 
1.3%
NOVA FRIBURGO 1938
 
1.2%
SÃO GONÇALO 1855
 
1.1%
TERESÓPOLIS 1786
 
1.1%
Other values (85) 46743
28.2%
(Missing) 89465
53.9%

Length

2022-12-14T11:38:26.144105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 10496
 
6.8%
rio 8609
 
5.6%
dos 6588
 
4.3%
são 6197
 
4.0%
janeiro 6136
 
4.0%
campos 5203
 
3.4%
goytacazes 5203
 
3.4%
barra 4962
 
3.2%
do 3504
 
2.3%
mansa 2989
 
1.9%
Other values (128) 94034
61.1%

Most occurring characters

ValueCountFrequency (%)
A 127487
13.9%
O 99206
10.9%
R 83268
 
9.1%
77343
 
8.5%
E 62500
 
6.8%
S 59969
 
6.6%
I 59648
 
6.5%
D 41984
 
4.6%
N 32474
 
3.6%
T 30768
 
3.4%
Other values (25) 239281
26.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 836437
91.5%
Space Separator 77343
 
8.5%
Dash Punctuation 148
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 127487
15.2%
O 99206
11.9%
R 83268
 
10.0%
E 62500
 
7.5%
S 59969
 
7.2%
I 59648
 
7.1%
D 41984
 
5.0%
N 32474
 
3.9%
T 30768
 
3.7%
M 27446
 
3.3%
Other values (23) 211687
25.3%
Space Separator
ValueCountFrequency (%)
77343
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 836437
91.5%
Common 77491
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 127487
15.2%
O 99206
11.9%
R 83268
 
10.0%
E 62500
 
7.5%
S 59969
 
7.2%
I 59648
 
7.1%
D 41984
 
5.0%
N 32474
 
3.9%
T 30768
 
3.7%
M 27446
 
3.3%
Other values (23) 211687
25.3%
Common
ValueCountFrequency (%)
77343
99.8%
- 148
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 881447
96.4%
None 32481
 
3.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 127487
14.5%
O 99206
11.3%
R 83268
9.4%
77343
 
8.8%
E 62500
 
7.1%
S 59969
 
6.8%
I 59648
 
6.8%
D 41984
 
4.8%
N 32474
 
3.7%
T 30768
 
3.5%
Other values (16) 206800
23.5%
None
ValueCountFrequency (%)
à 9697
29.9%
Ó 6199
19.1%
É 4333
13.3%
Ç 4090
12.6%
Í 3557
 
11.0%
Á 1953
 
6.0%
Ê 1480
 
4.6%
Ú 704
 
2.2%
Ô 468
 
1.4%

SQ_CANDIDATO_DOADOR
Real number (ℝ)

HIGH CORRELATION
MISSING
SKEWED

Distinct8826
Distinct (%)11.4%
Missing88401
Missing (%)53.2%
Infinite0
Infinite (%)0.0%
Mean1.9000707 × 1011
Minimum1.300016 × 1011
Maximum2.8000063 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:26.253127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.300016 × 1011
5-th percentile1.9000061 × 1011
Q11.9000066 × 1011
median1.9000085 × 1011
Q31.9000108 × 1011
95-th percentile1.9000124 × 1011
Maximum2.8000063 × 1011
Range1.4999903 × 1011
Interquartile range (IQR)425691.75

Descriptive statistics

Standard deviation8.1992815 × 108
Coefficient of variation (CV)0.0043152507
Kurtosis11582.438
Mean1.9000707 × 1011
Median Absolute Deviation (MAD)208698.5
Skewness97.134321
Sum1.4752529 × 1016
Variance6.7228217 × 1017
MonotonicityNot monotonic
2022-12-14T11:38:26.373258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190001081726.0 2161
 
1.3%
190000609934.0 1786
 
1.1%
190000852962.0 1729
 
1.0%
190000612301.0 1244
 
0.7%
190001131601.0 1154
 
0.7%
190000863537.0 828
 
0.5%
190001131229.0 808
 
0.5%
190000662122.0 767
 
0.5%
190000706493.0 749
 
0.5%
190001224983.0 749
 
0.5%
Other values (8816) 65667
39.5%
(Missing) 88401
53.2%
ValueCountFrequency (%)
130001598359.0 1
 
< 0.1%
190000601071.0 16
< 0.1%
190000601072.0 1
 
< 0.1%
190000601073.0 7
< 0.1%
190000601074.0 2
 
< 0.1%
190000601075.0 3
 
< 0.1%
190000601078.0 11
< 0.1%
190000601081.0 1
 
< 0.1%
190000601083.0 4
 
< 0.1%
190000601084.0 2
 
< 0.1%
ValueCountFrequency (%)
280000629808.0 1
 
< 0.1%
280000622281.0 1
 
< 0.1%
280000614517.0 4
 
< 0.1%
190001739242.0 1
 
< 0.1%
190001737551.0 2
 
< 0.1%
190001732486.0 1
 
< 0.1%
190001732303.0 1
 
< 0.1%
190001731347.0 1
 
< 0.1%
190001731148.0 2
 
< 0.1%
190001730176.0 319
0.2%

NR_CANDIDATO_DOADOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct3828
Distinct (%)4.9%
Missing87152
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean7649.999
Minimum10
Maximum90999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:26.498253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q120
median36
Q34439
95-th percentile51067.5
Maximum90999
Range90989
Interquartile range (IQR)4419

Descriptive statistics

Standard deviation17032.044
Coefficient of variation (CV)2.2264112
Kurtosis7.4401373
Mean7649.999
Median Absolute Deviation (MAD)24
Skewness2.7565481
Sum6.0351607 × 108
Variance2.9009052 × 108
MonotonicityNot monotonic
2022-12-14T11:38:26.610262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 6030
 
3.6%
55 5613
 
3.4%
22 5320
 
3.2%
25 5192
 
3.1%
12 4854
 
2.9%
15 3742
 
2.3%
23 3232
 
1.9%
11 3125
 
1.9%
77 3085
 
1.9%
13 2780
 
1.7%
Other values (3818) 35918
21.6%
(Missing) 87152
52.5%
ValueCountFrequency (%)
10 2065
1.2%
11 3125
1.9%
12 4854
2.9%
13 2780
1.7%
14 252
 
0.2%
15 3742
2.3%
16 37
 
< 0.1%
17 917
 
0.6%
18 205
 
0.1%
19 515
 
0.3%
ValueCountFrequency (%)
90999 25
< 0.1%
90990 1
 
< 0.1%
90963 1
 
< 0.1%
90951 2
 
< 0.1%
90909 6
 
< 0.1%
90901 1
 
< 0.1%
90900 10
 
< 0.1%
90888 3
 
< 0.1%
90852 3
 
< 0.1%
90800 3
 
< 0.1%

CD_CARGO_CANDIDATO_DOADOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)< 0.1%
Missing87152
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean9.9948537
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:26.699260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median11
Q311
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7343626
Coefficient of variation (CV)0.27357705
Kurtosis0.36616807
Mean9.9948537
Median Absolute Deviation (MAD)0
Skewness-1.1974585
Sum788504
Variance7.4767386
MonotonicityNot monotonic
2022-12-14T11:38:26.782257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
11 46383
27.9%
13 12557
 
7.6%
6 9477
 
5.7%
7 4399
 
2.6%
3 4298
 
2.6%
5 832
 
0.5%
12 815
 
0.5%
4 113
 
0.1%
9 7
 
< 0.1%
1 6
 
< 0.1%
(Missing) 87152
52.5%
ValueCountFrequency (%)
1 6
 
< 0.1%
3 4298
 
2.6%
4 113
 
0.1%
5 832
 
0.5%
6 9477
 
5.7%
7 4399
 
2.6%
9 7
 
< 0.1%
10 4
 
< 0.1%
11 46383
27.9%
12 815
 
0.5%
ValueCountFrequency (%)
13 12557
 
7.6%
12 815
 
0.5%
11 46383
27.9%
10 4
 
< 0.1%
9 7
 
< 0.1%
7 4399
 
2.6%
6 9477
 
5.7%
5 832
 
0.5%
4 113
 
0.1%
3 4298
 
2.6%

DS_CARGO_CANDIDATO_DOADOR
Categorical

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)< 0.1%
Missing87152
Missing (%)52.5%
Memory size8.3 MiB
Prefeito
46383 
Vereador
12557 
Deputado Federal
9477 
Deputado Estadual
 
4399
Governador
 
4298
Other values (6)
 
1777

Length

Max length17
Median length8
Mean length9.6235312
Min length7

Characters and Unicode

Total characters759210
Distinct characters28
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeputado Estadual
2nd rowDeputado Estadual
3rd rowDeputado Federal
4th rowDeputado Federal
5th rowDeputado Estadual

Common Values

ValueCountFrequency (%)
Prefeito 46383
27.9%
Vereador 12557
 
7.6%
Deputado Federal 9477
 
5.7%
Deputado Estadual 4399
 
2.6%
Governador 4298
 
2.6%
Senador 832
 
0.5%
Vice-prefeito 815
 
0.5%
Vice-governador 113
 
0.1%
1º Suplente 7
 
< 0.1%
Presidente 6
 
< 0.1%
(Missing) 87152
52.5%

Length

2022-12-14T11:38:26.872252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prefeito 46383
50.0%
deputado 13876
 
15.0%
vereador 12557
 
13.5%
federal 9477
 
10.2%
estadual 4399
 
4.7%
governador 4298
 
4.6%
senador 832
 
0.9%
vice-prefeito 815
 
0.9%
vice-governador 113
 
0.1%
suplente 11
 
< 0.1%
Other values (3) 17
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 158551
20.9%
r 91449
12.0%
o 83285
11.0%
t 65490
8.6%
a 49951
 
6.6%
i 48132
 
6.3%
f 47198
 
6.2%
P 46389
 
6.1%
d 45558
 
6.0%
u 18286
 
2.4%
Other values (18) 104921
13.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 651606
85.8%
Uppercase Letter 92767
 
12.2%
Space Separator 13887
 
1.8%
Dash Punctuation 928
 
0.1%
Other Letter 11
 
< 0.1%
Decimal Number 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 158551
24.3%
r 91449
14.0%
o 83285
12.8%
t 65490
10.1%
a 49951
 
7.7%
i 48132
 
7.4%
f 47198
 
7.2%
d 45558
 
7.0%
u 18286
 
2.8%
p 14702
 
2.3%
Other values (6) 29004
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
P 46389
50.0%
D 13876
 
15.0%
V 13485
 
14.5%
F 9477
 
10.2%
E 4399
 
4.7%
G 4298
 
4.6%
S 843
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 7
63.6%
2 4
36.4%
Space Separator
ValueCountFrequency (%)
13887
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 928
100.0%
Other Letter
ValueCountFrequency (%)
º 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 744384
98.0%
Common 14826
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 158551
21.3%
r 91449
12.3%
o 83285
11.2%
t 65490
8.8%
a 49951
 
6.7%
i 48132
 
6.5%
f 47198
 
6.3%
P 46389
 
6.2%
d 45558
 
6.1%
u 18286
 
2.5%
Other values (14) 90095
12.1%
Common
ValueCountFrequency (%)
13887
93.7%
- 928
 
6.3%
1 7
 
< 0.1%
2 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 759199
> 99.9%
None 11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 158551
20.9%
r 91449
12.0%
o 83285
11.0%
t 65490
8.6%
a 49951
 
6.6%
i 48132
 
6.3%
f 47198
 
6.2%
P 46389
 
6.1%
d 45558
 
6.0%
u 18286
 
2.4%
Other values (17) 104910
13.8%
None
ValueCountFrequency (%)
º 11
100.0%

NR_PARTIDO_DOADOR
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct36
Distinct (%)< 0.1%
Missing50202
Missing (%)30.2%
Infinite0
Infinite (%)0.0%
Mean29.857356
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:26.969258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q115
median22
Q344
95-th percentile77
Maximum90
Range80
Interquartile range (IQR)29

Descriptive statistics

Standard deviation19.832443
Coefficient of variation (CV)0.66423975
Kurtosis0.50628602
Mean29.857356
Median Absolute Deviation (MAD)9
Skewness1.1887696
Sum3458706
Variance393.32579
MonotonicityNot monotonic
2022-12-14T11:38:27.075262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
55 10400
 
6.3%
25 9564
 
5.8%
20 8741
 
5.3%
22 8352
 
5.0%
12 7205
 
4.3%
11 6838
 
4.1%
15 6828
 
4.1%
10 5891
 
3.5%
23 5158
 
3.1%
13 5119
 
3.1%
Other values (26) 41745
25.1%
(Missing) 50202
30.2%
ValueCountFrequency (%)
10 5891
3.5%
11 6838
4.1%
12 7205
4.3%
13 5119
3.1%
14 3259
2.0%
15 6828
4.1%
16 143
 
0.1%
17 3675
2.2%
18 1130
 
0.7%
19 1717
 
1.0%
ValueCountFrequency (%)
90 1720
 
1.0%
80 101
 
0.1%
77 5035
3.0%
70 1376
 
0.8%
65 1391
 
0.8%
55 10400
6.3%
54 121
 
0.1%
51 2178
 
1.3%
50 2871
 
1.7%
45 3086
 
1.9%

SG_PARTIDO_DOADOR
Categorical

HIGH CORRELATION
MISSING

Distinct41
Distinct (%)< 0.1%
Missing50202
Missing (%)30.2%
Memory size8.7 MiB
PSD
10400 
DEM
9564 
PSC
8741 
PDT
 
7205
PL
 
7115
Other values (36)
72816 

Length

Max length13
Median length3
Mean length4.1397605
Min length2

Characters and Unicode

Total characters479554
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREDE
2nd rowREDE
3rd rowPT
4th rowPT
5th rowPT

Common Values

ValueCountFrequency (%)
PSD 10400
 
6.3%
DEM 9564
 
5.8%
PSC 8741
 
5.3%
PDT 7205
 
4.3%
PL 7115
 
4.3%
PP 6838
 
4.1%
MDB 6828
 
4.1%
PT 5119
 
3.1%
SOLIDARIEDADE 5035
 
3.0%
CIDADANIA 4986
 
3.0%
Other values (31) 44010
26.5%
(Missing) 50202
30.2%

Length

2022-12-14T11:38:27.193256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
psd 10400
 
8.8%
dem 9564
 
8.1%
psc 8741
 
7.4%
pdt 7205
 
6.1%
pl 7115
 
6.0%
pp 6838
 
5.8%
mdb 6828
 
5.8%
pt 5119
 
4.3%
solidariedade 5035
 
4.2%
cidadania 4986
 
4.2%
Other values (33) 46792
39.4%

Most occurring characters

ValueCountFrequency (%)
P 90583
18.9%
D 67526
14.1%
S 42841
8.9%
A 36926
7.7%
E 29556
 
6.2%
I 27617
 
5.8%
B 25840
 
5.4%
T 24068
 
5.0%
L 23386
 
4.9%
C 23280
 
4.9%
Other values (12) 87931
18.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 473990
98.8%
Space Separator 2782
 
0.6%
Lowercase Letter 2782
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 90583
19.1%
D 67526
14.2%
S 42841
9.0%
A 36926
7.8%
E 29556
 
6.2%
I 27617
 
5.8%
B 25840
 
5.5%
T 24068
 
5.1%
L 23386
 
4.9%
C 23280
 
4.9%
Other values (9) 82367
17.4%
Lowercase Letter
ValueCountFrequency (%)
d 1391
50.0%
o 1391
50.0%
Space Separator
ValueCountFrequency (%)
2782
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 476772
99.4%
Common 2782
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 90583
19.0%
D 67526
14.2%
S 42841
9.0%
A 36926
7.7%
E 29556
 
6.2%
I 27617
 
5.8%
B 25840
 
5.4%
T 24068
 
5.0%
L 23386
 
4.9%
C 23280
 
4.9%
Other values (11) 85149
17.9%
Common
ValueCountFrequency (%)
2782
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 478947
99.9%
None 607
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 90583
18.9%
D 67526
14.1%
S 42841
8.9%
A 36926
7.7%
E 29556
 
6.2%
I 27617
 
5.8%
B 25840
 
5.4%
T 24068
 
5.0%
L 23386
 
4.9%
C 23280
 
4.9%
Other values (11) 87324
18.2%
None
ValueCountFrequency (%)
à 607
100.0%

NM_PARTIDO_DOADOR
Categorical

HIGH CORRELATION
MISSING

Distinct42
Distinct (%)< 0.1%
Missing50202
Missing (%)30.2%
Memory size11.5 MiB
Partido Social Democrático
10400 
Democratas
9564 
Partido Social Cristão
8741 
Partido Democrático Trabalhista
 
7205
Partido Liberal
 
7115
Other values (37)
72816 

Length

Max length46
Median length32
Mean length21.211402
Min length4

Characters and Unicode

Total characters2457150
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRede Sustentabilidade
2nd rowRede Sustentabilidade
3rd rowPartido dos Trabalhadores
4th rowPartido dos Trabalhadores
5th rowPartido dos Trabalhadores

Common Values

ValueCountFrequency (%)
Partido Social Democrático 10400
 
6.3%
Democratas 9564
 
5.8%
Partido Social Cristão 8741
 
5.3%
Partido Democrático Trabalhista 7205
 
4.3%
Partido Liberal 7115
 
4.3%
Movimento Democrático Brasileiro 6828
 
4.1%
PROGRESSISTAS 5854
 
3.5%
Partido dos Trabalhadores 5119
 
3.1%
Solidariedade 5035
 
3.0%
Cidadania 4986
 
3.0%
Other values (32) 44994
27.1%
(Missing) 50202
30.2%

Length

2022-12-14T11:38:27.302253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
partido 68172
24.3%
social 27622
 
9.9%
democrático 24433
 
8.7%
brasileiro 15088
 
5.4%
trabalhista 13074
 
4.7%
liberal 10790
 
3.9%
cristão 9664
 
3.4%
democratas 9564
 
3.4%
da 9396
 
3.4%
progressistas 6838
 
2.4%
Other values (41) 85534
30.5%

Most occurring characters

ValueCountFrequency (%)
a 284810
11.6%
i 260597
 
10.6%
o 244089
 
9.9%
r 216386
 
8.8%
164334
 
6.7%
t 146688
 
6.0%
d 132063
 
5.4%
e 122921
 
5.0%
c 108407
 
4.4%
l 98707
 
4.0%
Other values (34) 678148
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1898048
77.2%
Uppercase Letter 394768
 
16.1%
Space Separator 164334
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 284810
15.0%
i 260597
13.7%
o 244089
12.9%
r 216386
11.4%
t 146688
7.7%
d 132063
7.0%
e 122921
6.5%
c 108407
 
5.7%
l 98707
 
5.2%
s 82152
 
4.3%
Other values (14) 201228
10.6%
Uppercase Letter
ValueCountFrequency (%)
P 84400
21.4%
S 67994
17.2%
D 39602
10.0%
B 26447
 
6.7%
T 25566
 
6.5%
R 24733
 
6.3%
C 23280
 
5.9%
L 18958
 
4.8%
A 14003
 
3.5%
O 12855
 
3.3%
Other values (9) 56930
14.4%
Space Separator
ValueCountFrequency (%)
164334
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2292816
93.3%
Common 164334
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 284810
12.4%
i 260597
11.4%
o 244089
10.6%
r 216386
 
9.4%
t 146688
 
6.4%
d 132063
 
5.8%
e 122921
 
5.4%
c 108407
 
4.7%
l 98707
 
4.3%
P 84400
 
3.7%
Other values (33) 593748
25.9%
Common
ValueCountFrequency (%)
164334
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2416346
98.3%
None 40804
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 284810
11.8%
i 260597
10.8%
o 244089
 
10.1%
r 216386
 
9.0%
164334
 
6.8%
t 146688
 
6.1%
d 132063
 
5.5%
e 122921
 
5.1%
c 108407
 
4.5%
l 98707
 
4.1%
Other values (29) 637344
26.4%
None
ValueCountFrequency (%)
á 24659
60.4%
ã 13242
32.5%
ú 1237
 
3.0%
ç 1059
 
2.6%
à 607
 
1.5%

NR_RECIBO_DOACAO
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct93722
Distinct (%)92.3%
Missing64522
Missing (%)38.9%
Memory size10.0 MiB
000551158041RJ000030E
 
15
000551158041RJ000031E
 
15
065650600000RJ000102E
 
14
402751358777RJ000001E
 
13
000551158041RJ000028E
 
12
Other values (93717)
101452 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters2131941
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88492 ?
Unique (%)87.2%

Sample

1st row444440700000RJ000007E
2nd row444440700000RJ000003E
3rd row444440700000RJ000004E
4th row444440700000RJ000006E
5th row444440700000RJ000005E

Common Values

ValueCountFrequency (%)
000551158041RJ000030E 15
 
< 0.1%
000551158041RJ000031E 15
 
< 0.1%
065650600000RJ000102E 14
 
< 0.1%
402751358777RJ000001E 13
 
< 0.1%
000551158041RJ000028E 12
 
< 0.1%
000201158572RJ000002E 12
 
< 0.1%
000551158041RJ000027E 12
 
< 0.1%
000551158041RJ000024E 12
 
< 0.1%
000551158041RJ000025E 12
 
< 0.1%
000551158041RJ000023E 12
 
< 0.1%
Other values (93712) 101392
61.1%
(Missing) 64522
38.9%

Length

2022-12-14T11:38:27.401246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
000551158041rj000030e 15
 
< 0.1%
000551158041rj000031e 15
 
< 0.1%
065650600000rj000102e 14
 
< 0.1%
402751358777rj000001e 13
 
< 0.1%
000551158041rj000028e 12
 
< 0.1%
000201158572rj000002e 12
 
< 0.1%
000551158041rj000027e 12
 
< 0.1%
000551158041rj000024e 12
 
< 0.1%
000551158041rj000025e 12
 
< 0.1%
000551158041rj000023e 12
 
< 0.1%
Other values (93712) 101392
99.9%

Most occurring characters

ValueCountFrequency (%)
0 812135
38.1%
1 239037
 
11.2%
3 156711
 
7.4%
5 155649
 
7.3%
2 108051
 
5.1%
E 101521
 
4.8%
R 101478
 
4.8%
J 101473
 
4.8%
7 93702
 
4.4%
8 89399
 
4.2%
Other values (11) 172785
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1827378
85.7%
Uppercase Letter 304495
 
14.3%
Lowercase Letter 68
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 812135
44.4%
1 239037
 
13.1%
3 156711
 
8.6%
5 155649
 
8.5%
2 108051
 
5.9%
7 93702
 
5.1%
8 89399
 
4.9%
6 63836
 
3.5%
4 57001
 
3.1%
9 51857
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
E 101521
33.3%
R 101478
33.3%
J 101473
33.3%
A 7
 
< 0.1%
N 5
 
< 0.1%
C 5
 
< 0.1%
M 2
 
< 0.1%
G 2
 
< 0.1%
P 2
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
r 34
50.0%
j 34
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1827378
85.7%
Latin 304563
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 101521
33.3%
R 101478
33.3%
J 101473
33.3%
r 34
 
< 0.1%
j 34
 
< 0.1%
A 7
 
< 0.1%
N 5
 
< 0.1%
C 5
 
< 0.1%
M 2
 
< 0.1%
G 2
 
< 0.1%
Common
ValueCountFrequency (%)
0 812135
44.4%
1 239037
 
13.1%
3 156711
 
8.6%
5 155649
 
8.5%
2 108051
 
5.9%
7 93702
 
5.1%
8 89399
 
4.9%
6 63836
 
3.5%
4 57001
 
3.1%
9 51857
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2131941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 812135
38.1%
1 239037
 
11.2%
3 156711
 
7.4%
5 155649
 
7.3%
2 108051
 
5.1%
E 101521
 
4.8%
R 101478
 
4.8%
J 101473
 
4.8%
7 93702
 
4.4%
8 89399
 
4.2%
Other values (11) 172785
 
8.1%

NR_DOCUMENTO_DOACAO
Categorical

HIGH CARDINALITY
MISSING

Distinct36402
Distinct (%)59.6%
Missing104954
Missing (%)63.2%
Memory size7.8 MiB
SN
 
2702
1
 
994
01
 
723
001
 
701
0001
 
648
Other values (36397)
55321 

Length

Max length32
Median length25
Mean length7.7449295
Min length1

Characters and Unicode

Total characters473130
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31645 ?
Unique (%)51.8%

Sample

1st row4492717
2nd row1151559
3rd row1156105
4th row1220428
5th row1201250

Common Values

ValueCountFrequency (%)
SN 2702
 
1.6%
1 994
 
0.6%
01 723
 
0.4%
001 701
 
0.4%
0001 648
 
0.4%
000001 632
 
0.4%
TED 435
 
0.3%
00001 342
 
0.2%
000341 268
 
0.2%
4175 255
 
0.2%
Other values (36392) 53389
32.2%
(Missing) 104954
63.2%

Length

2022-12-14T11:38:27.507246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sn 2707
 
4.4%
1 994
 
1.6%
01 723
 
1.2%
001 701
 
1.1%
0001 648
 
1.1%
000001 632
 
1.0%
ted 435
 
0.7%
00001 342
 
0.6%
000341 268
 
0.4%
4175 255
 
0.4%
Other values (36233) 53387
87.4%

Most occurring characters

ValueCountFrequency (%)
0 105489
22.3%
1 55188
11.7%
2 40473
 
8.6%
5 37145
 
7.9%
3 36048
 
7.6%
7 34698
 
7.3%
4 34185
 
7.2%
8 31545
 
6.7%
6 31226
 
6.6%
9 31075
 
6.6%
Other values (41) 36058
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 437072
92.4%
Uppercase Letter 35315
 
7.5%
Space Separator 583
 
0.1%
Other Punctuation 126
 
< 0.1%
Lowercase Letter 34
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 4350
12.3%
D 3911
11.1%
N 3549
10.0%
B 3481
9.9%
A 3453
9.8%
S 3409
9.7%
C 3042
8.6%
F 2865
8.1%
T 1896
5.4%
I 916
 
2.6%
Other values (16) 4443
12.6%
Lowercase Letter
ValueCountFrequency (%)
c 6
17.6%
n 5
14.7%
s 5
14.7%
a 3
8.8%
e 3
8.8%
b 3
8.8%
f 2
 
5.9%
o 2
 
5.9%
u 1
 
2.9%
g 1
 
2.9%
Other values (3) 3
8.8%
Decimal Number
ValueCountFrequency (%)
0 105489
24.1%
1 55188
12.6%
2 40473
 
9.3%
5 37145
 
8.5%
3 36048
 
8.2%
7 34698
 
7.9%
4 34185
 
7.8%
8 31545
 
7.2%
6 31226
 
7.1%
9 31075
 
7.1%
Space Separator
ValueCountFrequency (%)
583
100.0%
Other Punctuation
ValueCountFrequency (%)
. 126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 437781
92.5%
Latin 35349
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 4350
12.3%
D 3911
11.1%
N 3549
10.0%
B 3481
9.8%
A 3453
9.8%
S 3409
9.6%
C 3042
8.6%
F 2865
8.1%
T 1896
5.4%
I 916
 
2.6%
Other values (29) 4477
12.7%
Common
ValueCountFrequency (%)
0 105489
24.1%
1 55188
12.6%
2 40473
 
9.2%
5 37145
 
8.5%
3 36048
 
8.2%
7 34698
 
7.9%
4 34185
 
7.8%
8 31545
 
7.2%
6 31226
 
7.1%
9 31075
 
7.1%
Other values (2) 709
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105489
22.3%
1 55188
11.7%
2 40473
 
8.6%
5 37145
 
7.9%
3 36048
 
7.6%
7 34698
 
7.3%
4 34185
 
7.2%
8 31545
 
6.7%
6 31226
 
6.6%
9 31075
 
6.6%
Other values (41) 36058
 
7.6%

SQ_RECEITA
Real number (ℝ)

Distinct155587
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18746224
Minimum10656486
Maximum32314862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:27.624245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10656486
5-th percentile12053796
Q117159154
median18755862
Q319794370
95-th percentile29386437
Maximum32314862
Range21658376
Interquartile range (IQR)2635216

Descriptive statistics

Standard deviation4799477.5
Coefficient of variation (CV)0.25602369
Kurtosis0.78661422
Mean18746224
Median Absolute Deviation (MAD)1184234
Skewness0.8428646
Sum3.1126793 × 1012
Variance2.3034985 × 1013
MonotonicityNot monotonic
2022-12-14T11:38:27.737249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28873326 32
 
< 0.1%
12311666 31
 
< 0.1%
12454140 18
 
< 0.1%
18958514 15
 
< 0.1%
19644306 15
 
< 0.1%
18958513 15
 
< 0.1%
19685039 15
 
< 0.1%
19644305 15
 
< 0.1%
30251995 14
 
< 0.1%
12319050 14
 
< 0.1%
Other values (155577) 165859
99.9%
ValueCountFrequency (%)
10656486 1
< 0.1%
10656487 1
< 0.1%
10661816 1
< 0.1%
10669283 1
< 0.1%
10669284 1
< 0.1%
10678535 1
< 0.1%
10683762 1
< 0.1%
10683763 1
< 0.1%
10691779 1
< 0.1%
10691780 1
< 0.1%
ValueCountFrequency (%)
32314862 1
 
< 0.1%
32314861 1
 
< 0.1%
32307980 1
 
< 0.1%
32307979 1
 
< 0.1%
32307978 5
< 0.1%
32307977 1
 
< 0.1%
32305712 1
 
< 0.1%
32305711 1
 
< 0.1%
32305710 1
 
< 0.1%
32305709 1
 
< 0.1%

DT_RECEITA
Categorical

Distinct430
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
21/10/2020
 
6578
13/11/2020
 
5046
19/10/2020
 
4245
15/10/2020
 
3950
20/10/2020
 
3936
Other values (425)
142288 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1660430
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)< 0.1%

Sample

1st row05/10/2022
2nd row05/10/2022
3rd row05/10/2022
4th row05/10/2022
5th row05/10/2022

Common Values

ValueCountFrequency (%)
21/10/2020 6578
 
4.0%
13/11/2020 5046
 
3.0%
19/10/2020 4245
 
2.6%
15/10/2020 3950
 
2.4%
20/10/2020 3936
 
2.4%
22/10/2020 3619
 
2.2%
11/11/2020 3257
 
2.0%
12/11/2020 3197
 
1.9%
26/10/2020 3159
 
1.9%
09/11/2020 3120
 
1.9%
Other values (420) 125936
75.8%

Length

2022-12-14T11:38:27.840734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21/10/2020 6578
 
4.0%
13/11/2020 5046
 
3.0%
19/10/2020 4245
 
2.6%
15/10/2020 3950
 
2.4%
20/10/2020 3936
 
2.4%
22/10/2020 3619
 
2.2%
11/11/2020 3257
 
2.0%
12/11/2020 3197
 
1.9%
26/10/2020 3159
 
1.9%
09/11/2020 3120
 
1.9%
Other values (420) 125936
75.8%

Most occurring characters

ValueCountFrequency (%)
0 473318
28.5%
2 388313
23.4%
/ 332086
20.0%
1 265189
16.0%
8 60236
 
3.6%
9 52445
 
3.2%
3 26881
 
1.6%
6 18859
 
1.1%
5 16931
 
1.0%
4 13803
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1328344
80.0%
Other Punctuation 332086
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 473318
35.6%
2 388313
29.2%
1 265189
20.0%
8 60236
 
4.5%
9 52445
 
3.9%
3 26881
 
2.0%
6 18859
 
1.4%
5 16931
 
1.3%
4 13803
 
1.0%
7 12369
 
0.9%
Other Punctuation
ValueCountFrequency (%)
/ 332086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1660430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 473318
28.5%
2 388313
23.4%
/ 332086
20.0%
1 265189
16.0%
8 60236
 
3.6%
9 52445
 
3.2%
3 26881
 
1.6%
6 18859
 
1.1%
5 16931
 
1.0%
4 13803
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1660430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 473318
28.5%
2 388313
23.4%
/ 332086
20.0%
1 265189
16.0%
8 60236
 
3.6%
9 52445
 
3.2%
3 26881
 
1.6%
6 18859
 
1.1%
5 16931
 
1.0%
4 13803
 
0.8%

DS_RECEITA
Categorical

HIGH CARDINALITY
MISSING

Distinct38701
Distinct (%)37.0%
Missing61517
Missing (%)37.0%
Memory size12.4 MiB
SANTINHOS
 
1469
SANTINHO
 
971
ADESIVO PERFURADO
 
694
SERVIÇOS CONTABEIS
 
579
ADESIVO
 
482
Other values (38696)
100331 

Length

Max length100
Median length79
Mean length33.291688
Min length1

Characters and Unicode

Total characters3479847
Distinct characters75
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32833 ?
Unique (%)31.4%

Sample

1st rowDOAÇÃO ESTIMAVEL PAJERO SPORT HPE ANO 2008 - 16/08/22 A 01/10/22
2nd rowDOAÇÃO ESTIMAVEL GOL 1.0 GIV ANO 2011 - 16/08/22 A 31/08/22
3rd rowDOAÇÃO ESTIMAVEL PALIO FIRE FLEX ANO 2018 - 16/08/22 A 01/10/22
4th rowDOAÇÃO ESTIMAVEL FOD ECOSPORT SE AT 1.5 ANO 2018 - 16/08/22 A 31/08/22
5th rowDOAÇÃO ESTIMAVEL FORD MODELO KS SEL 1.5 SD B ANO 2017 - 02/09/22 A 01/10/22

Common Values

ValueCountFrequency (%)
SANTINHOS 1469
 
0.9%
SANTINHO 971
 
0.6%
ADESIVO PERFURADO 694
 
0.4%
SERVIÇOS CONTABEIS 579
 
0.3%
ADESIVO 482
 
0.3%
SERVIÇOS ADVOCATICIOS 454
 
0.3%
ADESIVOS PERFURADOS 452
 
0.3%
SERVIÇOS CONTÁBEIS 430
 
0.3%
PANFLETAGEM 413
 
0.2%
ADVOGADO 385
 
0.2%
Other values (38691) 98197
59.1%
(Missing) 61517
37.0%

Length

2022-12-14T11:38:27.966474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 34898
 
6.6%
e 14226
 
2.7%
adesivo 12686
 
2.4%
12682
 
2.4%
santinhos 9171
 
1.7%
serviços 8912
 
1.7%
x 8553
 
1.6%
santinho 8049
 
1.5%
adesivos 7183
 
1.4%
cm 6798
 
1.3%
Other values (21053) 403548
76.6%

Most occurring characters

ValueCountFrequency (%)
433070
 
12.4%
A 315739
 
9.1%
O 281064
 
8.1%
E 259449
 
7.5%
I 213662
 
6.1%
R 201155
 
5.8%
S 189280
 
5.4%
D 155527
 
4.5%
N 134007
 
3.9%
C 123160
 
3.5%
Other values (65) 1173734
33.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2681221
77.0%
Space Separator 433070
 
12.4%
Decimal Number 296853
 
8.5%
Other Punctuation 51721
 
1.5%
Dash Punctuation 13090
 
0.4%
Math Symbol 1083
 
< 0.1%
Open Punctuation 1082
 
< 0.1%
Close Punctuation 1077
 
< 0.1%
Other Letter 408
 
< 0.1%
Currency Symbol 136
 
< 0.1%
Other values (2) 106
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 315739
11.8%
O 281064
10.5%
E 259449
 
9.7%
I 213662
 
8.0%
R 201155
 
7.5%
S 189280
 
7.1%
D 155527
 
5.8%
N 134007
 
5.0%
C 123160
 
4.6%
T 118543
 
4.4%
Other values (28) 689635
25.7%
Decimal Number
ValueCountFrequency (%)
0 82761
27.9%
1 46780
15.8%
4 36263
12.2%
5 29496
 
9.9%
2 21223
 
7.1%
8 19560
 
6.6%
9 17504
 
5.9%
7 16735
 
5.6%
3 15114
 
5.1%
6 11417
 
3.8%
Other Punctuation
ValueCountFrequency (%)
· 24892
48.1%
/ 18849
36.4%
. 7197
 
13.9%
: 477
 
0.9%
' 89
 
0.2%
? 87
 
0.2%
, 62
 
0.1%
* 54
 
0.1%
§ 11
 
< 0.1%
¡ 3
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1069
99.3%
] 6
 
0.6%
} 2
 
0.2%
Math Symbol
ValueCountFrequency (%)
+ 1015
93.7%
= 63
 
5.8%
| 5
 
0.5%
Currency Symbol
ValueCountFrequency (%)
$ 123
90.4%
£ 8
 
5.9%
¢ 5
 
3.7%
Open Punctuation
ValueCountFrequency (%)
( 1078
99.6%
[ 4
 
0.4%
Other Letter
ValueCountFrequency (%)
ª 241
59.1%
º 167
40.9%
Space Separator
ValueCountFrequency (%)
433070
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13090
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 105
100.0%
Other Number
ValueCountFrequency (%)
³ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2681629
77.1%
Common 798218
 
22.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 315739
11.8%
O 281064
10.5%
E 259449
 
9.7%
I 213662
 
8.0%
R 201155
 
7.5%
S 189280
 
7.1%
D 155527
 
5.8%
N 134007
 
5.0%
C 123160
 
4.6%
T 118543
 
4.4%
Other values (30) 690043
25.7%
Common
ValueCountFrequency (%)
433070
54.3%
0 82761
 
10.4%
1 46780
 
5.9%
4 36263
 
4.5%
5 29496
 
3.7%
· 24892
 
3.1%
2 21223
 
2.7%
8 19560
 
2.5%
/ 18849
 
2.4%
9 17504
 
2.2%
Other values (25) 67820
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3375410
97.0%
None 104437
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
433070
12.8%
A 315739
 
9.4%
O 281064
 
8.3%
E 259449
 
7.7%
I 213662
 
6.3%
R 201155
 
6.0%
S 189280
 
5.6%
D 155527
 
4.6%
N 134007
 
4.0%
C 123160
 
3.6%
Other values (45) 1069297
31.7%
None
ValueCountFrequency (%)
Ç 33927
32.5%
à 29274
28.0%
· 24892
23.8%
Á 4738
 
4.5%
Í 3759
 
3.6%
Õ 2409
 
2.3%
Ê 1859
 
1.8%
É 1132
 
1.1%
 699
 
0.7%
Ó 678
 
0.6%
Other values (10) 1070
 
1.0%

VR_RECEITA
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct8785
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5620.9566
Minimum0.01
Maximum8834000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2022-12-14T11:38:28.092476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile27.5
Q1110
median400
Q31200
95-th percentile11000
Maximum8834000
Range8834000
Interquartile range (IQR)1090

Descriptive statistics

Standard deviation59527.602
Coefficient of variation (CV)10.590297
Kurtosis4444.3226
Mean5620.9566
Median Absolute Deviation (MAD)350
Skewness49.421856
Sum9.333205 × 108
Variance3.5435353 × 109
MonotonicityNot monotonic
2022-12-14T11:38:28.209162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000.0 10635
 
6.4%
500.0 5164
 
3.1%
300.0 4144
 
2.5%
100.0 3987
 
2.4%
200.0 3887
 
2.3%
5000.0 3283
 
2.0%
2000.0 3216
 
1.9%
50.0 2818
 
1.7%
150.0 2801
 
1.7%
10000.0 2711
 
1.6%
Other values (8775) 123397
74.3%
ValueCountFrequency (%)
0.01 26
< 0.1%
0.02 8
 
< 0.1%
0.03 1
 
< 0.1%
0.04 4
 
< 0.1%
0.05 5
 
< 0.1%
0.06 5
 
< 0.1%
0.07 1
 
< 0.1%
0.08 3
 
< 0.1%
0.09 1
 
< 0.1%
0.1 7
 
< 0.1%
ValueCountFrequency (%)
8834000.0 1
 
< 0.1%
5000000.0 2
 
< 0.1%
4000000.0 1
 
< 0.1%
3500000.0 3
< 0.1%
3000000.0 6
< 0.1%
2650000.0 1
 
< 0.1%
2600000.0 1
 
< 0.1%
2550000.0 2
 
< 0.1%
2500000.0 5
< 0.1%
2000000.0 7
< 0.1%

Interactions

2022-12-14T11:38:11.157085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:19.716988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:22.827552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:25.727961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:28.796415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:31.350847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:34.101336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:36.857314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:39.575273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:41.700803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:44.624196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:47.423640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:50.309449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:52.859175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:55.738145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:58.325838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:00.766721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:03.215693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:05.739240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:08.285701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:11.295086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:19.923893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:22.978553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:25.882954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:28.931418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:31.499557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:34.239331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:37.022679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:39.675260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:41.841803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:44.770200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:47.569642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:50.437648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:53.020178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:55.858147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:58.444339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:00.879738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:03.547846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:05.868241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:08.429705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:11.444071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:20.086014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:23.150535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:26.051944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:29.072417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:31.666060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:34.386334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:37.179683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:39.781257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:41.995238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:44.927197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:47.726818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:50.576642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:53.186159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:55.989153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:58.575342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:01.003737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:03.678859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:06.008696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:08.579788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:11.578068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:20.227094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:23.300544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:26.209944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:29.202855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:31.817044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:34.522329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:37.325684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:39.889262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:42.137376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:45.071196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:47.868634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:50.703647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:53.338173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:56.106159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:58.703333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:01.123730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:03.798846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:06.138908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:08.717791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:11.713084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:20.374191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:23.450553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:26.369941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:29.337852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:31.960046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:34.655317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:37.469679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:39.998256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:42.459717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:45.215196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:48.013633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:50.830641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:53.492156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:56.236139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:58.827316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:01.233715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:03.913841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:06.268207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:08.858775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:11.848081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:20.519506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:23.600543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:26.527939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:29.468855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:32.103040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:34.791330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:37.619675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:40.100251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:42.600714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:45.360194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:48.160637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:50.958956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:53.647174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:56.353150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:58.960320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:01.350720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:04.029838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:06.398216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:09.025785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:11.985066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:20.665511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:23.754432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:26.679972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:29.603055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:32.249043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:34.923325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:37.768670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:40.210256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:42.739624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:45.503191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:48.305804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:51.082960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:53.812180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:56.654890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:59.077338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:01.457727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:04.142534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:06.525208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:09.168774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:12.079086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:20.765499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:23.859444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:26.782984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:29.699854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:32.350357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:35.018329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:37.868676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:40.311251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:42.840629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:45.607208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:48.414794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:51.185948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:53.919174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-14T11:37:55.300145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:57.973838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:00.411262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:02.828696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:05.380246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:07.884708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:10.745086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:13.509908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:22.556546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:25.438421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:28.518424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:31.089854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:33.822543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:36.590313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:39.338291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:41.453814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:44.342199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:47.138640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:50.049460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:52.595178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:55.466159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:58.097845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:00.538265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:02.973709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:05.504244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:08.015709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:10.891076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:13.637908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:22.688554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:25.581417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:28.659405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:31.217849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:33.961338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:36.720310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:39.480299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:41.563816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:44.484209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:47.287639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:50.184442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:52.715177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:55.621154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:37:58.213827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:00.652731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:03.093718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:05.617246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:08.138709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-14T11:38:11.023085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-14T11:38:28.562152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-14T11:38:28.935153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-14T11:38:29.206155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-14T11:38:29.475679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-14T11:38:29.741296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-14T11:38:30.032300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-14T11:38:14.510644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-14T11:38:15.289638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-14T11:38:16.810105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAOCD_ELEICAODS_ELEICAODT_ELEICAOST_TURNOTP_PRESTACAO_CONTASDT_PRESTACAO_CONTASSQ_PRESTADOR_CONTASSG_UFSG_UENM_UENR_CNPJ_PRESTADOR_CONTACD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONR_CPF_CANDIDATONR_CPF_VICE_CANDIDATONR_PARTIDOSG_PARTIDONM_PARTIDOCD_FONTE_RECEITADS_FONTE_RECEITACD_ORIGEM_RECEITADS_ORIGEM_RECEITACD_NATUREZA_RECEITADS_NATUREZA_RECEITACD_ESPECIE_RECEITADS_ESPECIE_RECEITACD_CNAE_DOADORDS_CNAE_DOADORNR_CPF_CNPJ_DOADORNM_DOADORNM_DOADOR_RFBCD_ESFERA_PARTIDARIA_DOADORDS_ESFERA_PARTIDARIA_DOADORSG_UF_DOADORCD_MUNICIPIO_DOADORNM_MUNICIPIO_DOADORSQ_CANDIDATO_DOADORNR_CANDIDATO_DOADORCD_CARGO_CANDIDATO_DOADORDS_CARGO_CANDIDATO_DOADORNR_PARTIDO_DOADORSG_PARTIDO_DOADORNM_PARTIDO_DOADORNR_RECIBO_DOACAONR_DOCUMENTO_DOACAOSQ_RECEITADT_RECEITADS_RECEITAVR_RECEITA
020222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL17/11/20223786148253RJRJRIO DE JANEIRO475081130001667Deputado Estadual19000161945644444FILIPE BEZERRA RIBEIRO SOARES5351633710<NA>44UNIÃOUNIÃO BRASIL1OUTROS RECURSOS10010200Recursos de pessoas físicas1FINANCEIRO1Transferência eletrônica<NA><NA>96918985787.0ANDRE LUIZ ISSA VIEIRAANDRE LUIZ ISSA VIEIRA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>444440700000RJ000007E44927173109890305/10/2022<NA>10000.0
120222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL17/11/20223786148253RJRJRIO DE JANEIRO475081130001667Deputado Estadual19000161945644444FILIPE BEZERRA RIBEIRO SOARES5351633710<NA>44UNIÃOUNIÃO BRASIL1OUTROS RECURSOS10010200Recursos de pessoas físicas1FINANCEIRO19PIX<NA><NA>10645791750.0ALEX DE PAULA OLIVEIRAALEX DE PAULA OLIVEIRA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>444440700000RJ000003E11515593109889905/10/2022<NA>7000.0
220222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL17/11/20223786148253RJRJRIO DE JANEIRO475081130001667Deputado Estadual19000161945644444FILIPE BEZERRA RIBEIRO SOARES5351633710<NA>44UNIÃOUNIÃO BRASIL1OUTROS RECURSOS10010200Recursos de pessoas físicas1FINANCEIRO19PIX<NA><NA>3357425726.0UBIRAJARA PIMENTEL CANDIDOUBIRAJARA PIMENTEL CANDIDO<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>444440700000RJ000004E11561053109890005/10/2022<NA>5000.0
320222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL17/11/20223786148253RJRJRIO DE JANEIRO475081130001667Deputado Estadual19000161945644444FILIPE BEZERRA RIBEIRO SOARES5351633710<NA>44UNIÃOUNIÃO BRASIL1OUTROS RECURSOS10010200Recursos de pessoas físicas1FINANCEIRO19PIX<NA><NA>2876581710.0CRISTIANO DE FREITAS NEVESCRISTIANO DE FREITAS NEVES<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>444440700000RJ000006E12204283109890205/10/2022<NA>12000.0
420222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL17/11/20223786148253RJRJRIO DE JANEIRO475081130001667Deputado Estadual19000161945644444FILIPE BEZERRA RIBEIRO SOARES5351633710<NA>44UNIÃOUNIÃO BRASIL1OUTROS RECURSOS10010200Recursos de pessoas físicas1FINANCEIRO19PIX<NA><NA>48288128734.0FERNANDO NASCENTES PIRESFERNANDO NASCENTES PIRES<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>444440700000RJ000005E12012503109890105/10/2022<NA>6000.0
520222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL01/11/20223786148444RJRJRIO DE JANEIRO475081090001066Deputado Federal1900016194804499CLEBIO LOPES PEREIRA5279010758<NA>44UNIÃOUNIÃO BRASIL1OUTROS RECURSOS10010200Recursos de pessoas físicas0ESTIMÁVEL2Estimado<NA><NA>73394904772.0GIOVANNA FRANCISCA LOREDANA ANGRILI DOS ANJOSGIOVANNA FRANCISCA LOREDANA ANGRILLI DOS ANJOS<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023605516/08/2022DOAÇÃO ESTIMAVEL PAJERO SPORT HPE ANO 2008 - 16/08/22 A 01/10/221950.0
620222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL01/11/20223786148444RJRJRIO DE JANEIRO475081090001066Deputado Federal1900016194804499CLEBIO LOPES PEREIRA5279010758<NA>44UNIÃOUNIÃO BRASIL1OUTROS RECURSOS10010200Recursos de pessoas físicas0ESTIMÁVEL2Estimado<NA><NA>3314612770.0TAMIRES DE SOUZA MACENATAMIRES DE SOUZA MACENA<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023605616/08/2022DOAÇÃO ESTIMAVEL GOL 1.0 GIV ANO 2011 - 16/08/22 A 31/08/221333.33
720222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL01/11/20223786148444RJRJRIO DE JANEIRO475081090001066Deputado Federal1900016194804499CLEBIO LOPES PEREIRA5279010758<NA>44UNIÃOUNIÃO BRASIL1OUTROS RECURSOS10010200Recursos de pessoas físicas0ESTIMÁVEL2Estimado<NA><NA>10581507746.0ANDRE LUIS DOS SANTOSANDRE LUIS DOS SANTOS<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023605316/08/2022DOAÇÃO ESTIMAVEL PALIO FIRE FLEX ANO 2018 - 16/08/22 A 01/10/223750.0
820222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL01/11/20223786148444RJRJRIO DE JANEIRO475081090001066Deputado Federal1900016194804499CLEBIO LOPES PEREIRA5279010758<NA>44UNIÃOUNIÃO BRASIL1OUTROS RECURSOS10010200Recursos de pessoas físicas0ESTIMÁVEL2Estimado<NA><NA>10340993707.0THIAGO DE LIMA ANDRADETHIAGO DE LIMA ANDRADE<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023605816/08/2022DOAÇÃO ESTIMAVEL FOD ECOSPORT SE AT 1.5 ANO 2018 - 16/08/22 A 31/08/22693.33
920222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL01/11/20223786148444RJRJRIO DE JANEIRO475081090001066Deputado Federal1900016194804499CLEBIO LOPES PEREIRA5279010758<NA>44UNIÃOUNIÃO BRASIL1OUTROS RECURSOS10010200Recursos de pessoas físicas0ESTIMÁVEL2Estimado<NA><NA>1628288701.0THAIS MARZANO RODRIGUES EMMEL ROSATHAIS MARZANO DE BARROS RODRIGUES EMMEL ROSA DE AGUIAR<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3023605702/09/2022DOAÇÃO ESTIMAVEL FORD MODELO KS SEL 1.5 SD B ANO 2017 - 02/09/22 A 01/10/221300.0
ANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAOCD_ELEICAODS_ELEICAODT_ELEICAOST_TURNOTP_PRESTACAO_CONTASDT_PRESTACAO_CONTASSQ_PRESTADOR_CONTASSG_UFSG_UENM_UENR_CNPJ_PRESTADOR_CONTACD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONR_CPF_CANDIDATONR_CPF_VICE_CANDIDATONR_PARTIDOSG_PARTIDONM_PARTIDOCD_FONTE_RECEITADS_FONTE_RECEITACD_ORIGEM_RECEITADS_ORIGEM_RECEITACD_NATUREZA_RECEITADS_NATUREZA_RECEITACD_ESPECIE_RECEITADS_ESPECIE_RECEITACD_CNAE_DOADORDS_CNAE_DOADORNR_CPF_CNPJ_DOADORNM_DOADORNM_DOADOR_RFBCD_ESFERA_PARTIDARIA_DOADORDS_ESFERA_PARTIDARIA_DOADORSG_UF_DOADORCD_MUNICIPIO_DOADORNM_MUNICIPIO_DOADORSQ_CANDIDATO_DOADORNR_CANDIDATO_DOADORCD_CARGO_CANDIDATO_DOADORDS_CARGO_CANDIDATO_DOADORNR_PARTIDO_DOADORSG_PARTIDO_DOADORNM_PARTIDO_DOADORNR_RECIBO_DOACAONR_DOCUMENTO_DOACAOSQ_RECEITADT_RECEITADS_RECEITAVR_RECEITA
16603320182ORDINÁRIA297Eleições Gerais Estaduais 201807/10/20181FINAL24/05/2019422500109RJRJRIO DE JANEIRO312084710001137Deputado Estadual19000061147190453MONIQUE NUNES ELIAS BRASILEIRO ROCHA8458777711<NA>90PROSPartido Republicano da Ordem Social0FUNDO PARTIDARIO10040000Recursos de outros candidatos1FINANCEIRO1Transferência eletrônica94928Atividades de organizações políticas31192740000109.0FELIPE LEONE BORNIERELEICAO 2018 FELIPE LEONE BORNIER DE OLIVEIRA DEPUTADO FEDERAL<NA><NA>RJ<NA><NA>190000607872.090906Deputado Federal90PROSPartido Republicano da Ordem Social<NA>003160151246721125/09/2018<NA>5000.0
16603420182ORDINÁRIA297Eleições Gerais Estaduais 201807/10/20181FINAL24/05/2019422500109RJRJRIO DE JANEIRO312084710001137Deputado Estadual19000061147190453MONIQUE NUNES ELIAS BRASILEIRO ROCHA8458777711<NA>90PROSPartido Republicano da Ordem Social2FUNDO ESPECIAL10040000Recursos de outros candidatos1FINANCEIRO1Transferência eletrônica94928Atividades de organizações políticas31192948000110.0NELSON ROBERTO BORNIERELEICAO 2018 NELSON ROBERTO BORNIER DE OLIVEIRA DEPUTADO FEDERAL<NA><NA>RJ<NA><NA>190000607877.090006Deputado Federal90PROSPartido Republicano da Ordem Social<NA>0047441246720706/09/2018<NA>5000.0
16603520182ORDINÁRIA297Eleições Gerais Estaduais 201807/10/20181FINAL24/05/2019422500109RJRJRIO DE JANEIRO312084710001137Deputado Estadual19000061147190453MONIQUE NUNES ELIAS BRASILEIRO ROCHA8458777711<NA>90PROSPartido Republicano da Ordem Social1OUTROS RECURSOS10040000Recursos de outros candidatos0ESTIMÁVEL2Estimado94928Atividades de organizações políticas31208533000197.0ELEIÇÕES 2018 WILSON JOSE WITZELELEICAO 2018 WILSON JOSE WITZEL GOVERNADOR<NA><NA>RJ<NA><NA>190000612301.0203Governador20PSCPartido Social Cristão<NA><NA>1246721318/08/2018CARDS ELEIÇÕES 2018· WILSON JOSE WITZEL + MONIQUE CONES +.NELSON BORNIER291.66
16603620182ORDINÁRIA297Eleições Gerais Estaduais 201807/10/20181FINAL24/05/2019422500109RJRJRIO DE JANEIRO312084710001137Deputado Estadual19000061147190453MONIQUE NUNES ELIAS BRASILEIRO ROCHA8458777711<NA>90PROSPartido Republicano da Ordem Social1OUTROS RECURSOS10040000Recursos de outros candidatos0ESTIMÁVEL2Estimado94928Atividades de organizações políticas31208588000105.0AMANDA SIQUEIRA NOVAESELEICAO 2018 AMANDA SIQUEIRA NOVAES DEPUTADO ESTADUAL<NA><NA>RJ<NA><NA>190000611513.0900217Deputado Estadual90PROSPartido Republicano da Ordem Social<NA><NA>1246721204/10/2018BANDEIRINHAS2800.0
16603720182ORDINÁRIA297Eleições Gerais Estaduais 201807/10/20181FINAL24/05/2019422500109RJRJRIO DE JANEIRO312084710001137Deputado Estadual19000061147190453MONIQUE NUNES ELIAS BRASILEIRO ROCHA8458777711<NA>90PROSPartido Republicano da Ordem Social1OUTROS RECURSOS10040000Recursos de outros candidatos0ESTIMÁVEL2Estimado94928Atividades de organizações políticas31208588000105.0AMANDA SIQUEIRA NOVAESELEICAO 2018 AMANDA SIQUEIRA NOVAES DEPUTADO ESTADUAL<NA><NA>RJ<NA><NA>190000611513.0900217Deputado Estadual90PROSPartido Republicano da Ordem Social<NA><NA>1246721204/10/2018SANTINHOS2800.0
16603820182ORDINÁRIA297Eleições Gerais Estaduais 201807/10/20181FINAL24/05/2019422500109RJRJRIO DE JANEIRO312084710001137Deputado Estadual19000061147190453MONIQUE NUNES ELIAS BRASILEIRO ROCHA8458777711<NA>90PROSPartido Republicano da Ordem Social1OUTROS RECURSOS10040000Recursos de outros candidatos0ESTIMÁVEL2Estimado94928Atividades de organizações políticas31208533000197.0ELEIÇÕES 2018 WILSON JOSE WITZELELEICAO 2018 WILSON JOSE WITZEL GOVERNADOR<NA><NA>RJ<NA><NA>190000612301.0203Governador20PSCPartido Social Cristão<NA><NA>1246720817/08/2018ADESIVOS MICROPERFURADOS 0.8X40M ELEIÇÃO 2018 WILSON JOSE WITZEL + MONIQUE CONES + NELSON BORNIER150.0
16603920182ORDINÁRIA297Eleições Gerais Estaduais 201807/10/20181FINAL24/05/2019422500109RJRJRIO DE JANEIRO312084710001137Deputado Estadual19000061147190453MONIQUE NUNES ELIAS BRASILEIRO ROCHA8458777711<NA>90PROSPartido Republicano da Ordem Social2FUNDO ESPECIAL10040000Recursos de outros candidatos1FINANCEIRO1Transferência eletrônica94928Atividades de organizações políticas31192948000110.0NELSON ROBERTO BORNIERELEICAO 2018 NELSON ROBERTO BORNIER DE OLIVEIRA DEPUTADO FEDERAL<NA><NA>RJ<NA><NA>190000607877.090006Deputado Federal90PROSPartido Republicano da Ordem Social<NA>0047551246720914/09/2018<NA>5000.0
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Duplicate rows

Most frequently occurring

ANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAOCD_ELEICAODS_ELEICAODT_ELEICAOST_TURNOTP_PRESTACAO_CONTASDT_PRESTACAO_CONTASSQ_PRESTADOR_CONTASSG_UFNM_UENR_CNPJ_PRESTADOR_CONTACD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONR_CPF_CANDIDATONR_CPF_VICE_CANDIDATONR_PARTIDOSG_PARTIDONM_PARTIDOCD_FONTE_RECEITADS_FONTE_RECEITACD_ORIGEM_RECEITADS_ORIGEM_RECEITACD_NATUREZA_RECEITADS_NATUREZA_RECEITACD_ESPECIE_RECEITADS_ESPECIE_RECEITACD_CNAE_DOADORDS_CNAE_DOADORNR_CPF_CNPJ_DOADORNM_DOADORNM_DOADOR_RFBCD_ESFERA_PARTIDARIA_DOADORDS_ESFERA_PARTIDARIA_DOADORSG_UF_DOADORCD_MUNICIPIO_DOADORNM_MUNICIPIO_DOADORSQ_CANDIDATO_DOADORNR_CANDIDATO_DOADORCD_CARGO_CANDIDATO_DOADORDS_CARGO_CANDIDATO_DOADORNR_PARTIDO_DOADORSG_PARTIDO_DOADORNM_PARTIDO_DOADORNR_RECIBO_DOACAONR_DOCUMENTO_DOACAOSQ_RECEITADT_RECEITADS_RECEITAVR_RECEITA# duplicates
1320202ORDINÁRIA426Eleições Municipais 202015/11/20201FINAL01/02/20211846998437RJJAPERI3908458300011113Vereador19000107005312000ROGERIO GOMES CASTRO12224171706<NA>12PDTPartido Democrático Trabalhista2FUNDO ESPECIAL10040000Recursos de outros candidatos0ESTIMÁVEL2Estimado94928Atividades de organizações políticas38759256000150.0FERNANDA MACHADO ONTIVEROSELEICAO 2020 FERNANDA MACHADO ONTIVEROS PREFEITO<NA><NA>RJ58149JAPERI190000865516.01211Prefeito12PDTPartido Democrático Trabalhista120001358149RJ000013E<NA>1967188008/10/2020ADESIVO PERFURADO 80X40153.079
4420202ORDINÁRIA426Eleições Municipais 202015/11/20201FINAL10/12/20201844151395RJMIGUEL PEREIRA3877288100013113Vereador19000087316411999VALDECIR BITTENCOURT78856221772<NA>11PPPROGRESSISTAS1OUTROS RECURSOS10020000Recursos de partido político0ESTIMÁVEL2Estimado94928Atividades de organizações políticas14099651000195.0Direção Municipal/Comissão ProvisóriaCOMISSAO MUNICIPAL PROVISORIA DO PSC EM MIGUEL PEREIRAMMunicipalRJ58572MIGUEL PEREIRA<NA><NA><NA><NA>20PSCPartido Social Cristão119991358572RJ000005E<NA>1784318011/11/2020ADESIVO PERFURADO 80X40 CM85.06
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6420202ORDINÁRIA426Eleições Municipais 202015/11/20201FINAL14/12/20201817706287RJTERESÓPOLIS3845104500015513Vereador19000063609127111ROCSILVAN RESENDE DA ROCHA914619799<NA>27DCDemocracia Cristã2FUNDO ESPECIAL10020000Recursos de partido político0ESTIMÁVEL2Estimado94928Atividades de organizações políticas15431473000110.0Direção Municipal/Comissão ProvisóriaPARTIDO SOCIAL DEMOCRATA CRISTAO - PSDCMMunicipalRJ59153TERESÓPOLIS<NA><NA><NA><NA>27DCDemocracia Cristã271111359153RJ000001E<NA>1841440504/11/2020MILHEIRO CARTÃO DE VISITA950.04
6620202ORDINÁRIA426Eleições Municipais 202015/11/20201FINAL14/12/20201817706287RJTERESÓPOLIS3845104500015513Vereador19000063609127111ROCSILVAN RESENDE DA ROCHA914619799<NA>27DCDemocracia Cristã2FUNDO ESPECIAL10020000Recursos de partido político0ESTIMÁVEL2Estimado94928Atividades de organizações políticas15431473000110.0Direção Municipal/Comissão ProvisóriaPARTIDO SOCIAL DEMOCRATA CRISTAO - PSDCMMunicipalRJ59153TERESÓPOLIS<NA><NA><NA><NA>27DCDemocracia Cristã271111359153RJ000001E<NA>1841440504/11/2020MILHEIRO SANTINHO300.04
9620202ORDINÁRIA426Eleições Municipais 202015/11/20201FINAL19/05/20211846998199RJJAPERI3897793800013913Vereador19000107004812180CARLOS JESUS ONTIVEROS GUARDIA18396364753<NA>12PDTPartido Democrático Trabalhista2FUNDO ESPECIAL10040000Recursos de outros candidatos0ESTIMÁVEL2Estimado94928Atividades de organizações políticas38759256000150.0FERNANDA MACHADO ONTIVEROSELEICAO 2020 FERNANDA MACHADO ONTIVEROS PREFEITO<NA><NA>RJ58149JAPERI190000865516.01211Prefeito12PDTPartido Democrático Trabalhista121801358149RJ000005E<NA>1988323408/10/2020ADESIVO PERFURADO 80X40153.074
11220222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL01/11/20223808247603RJRIO DE JANEIRO475824800001006Deputado Federal1900017235533545RUDSON VELASQUES GOMES7187585799<NA>35PMBPartido da Mulher Brasileira2FUNDO ESPECIAL10020000Recursos de partido político0ESTIMÁVEL2Estimado94928Atividades de organizações políticas23628747000194.0Direção Estadual/DistritalCOMISSAO PROVISORIA ESTADUAL DO PARTIDO DA MULHER BRASILEIRA - RJFFederal (Estadual/Distrital)RJ<NA><NA><NA><NA><NA><NA>35PMBPartido da Mulher Brasileira<NA><NA>3023165515/09/2022PUBLICIDADE MATERIAL IMPRESSO SANTINHOS202.54
11620222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL01/11/20223808248307RJRIO DE JANEIRO475824910001906Deputado Federal1900017235333552DEBORA OLIVEIRA DE MELO RICIO9140322750<NA>35PMBPartido da Mulher Brasileira2FUNDO ESPECIAL10020000Recursos de partido político0ESTIMÁVEL2Estimado94928Atividades de organizações políticas23628747000194.0Direção Estadual/DistritalCOMISSAO PROVISORIA ESTADUAL DO PARTIDO DA MULHER BRASILEIRA - RJFFederal (Estadual/Distrital)RJ<NA><NA><NA><NA><NA><NA>35PMBPartido da Mulher Brasileira<NA><NA>3023180915/09/2022PUBLICIDADE MATERIAL IMPRESSO SANTINHOS202.54
11920222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL03/11/20223778708606RJRIO DE JANEIRO474644440001416Deputado Federal1900016097397000SULAMITA DO CARMO DA SILVA48967068700<NA>70AVANTEAVANTE0FUNDO PARTIDARIO10020000Recursos de partido político0ESTIMÁVEL2Estimado94928Atividades de organizações políticas3917554000146.0Direção Municipal/Comissão ProvisóriaAVANTE - RIO DE JANEIRO - RJ - MUNICIPALMMunicipalRJ60011RIO DE JANEIRO<NA><NA><NA><NA>70AVANTEAVANTE<NA><NA>3025199529/09/2022ADESIVO LEITOSO 40X15350.04
12020222ORDINÁRIA546Eleições Gerais Estaduais 202202/10/20221FINAL03/11/20223778708606RJRIO DE JANEIRO474644440001416Deputado Federal1900016097397000SULAMITA DO CARMO DA SILVA48967068700<NA>70AVANTEAVANTE0FUNDO PARTIDARIO10020000Recursos de partido político0ESTIMÁVEL2Estimado94928Atividades de organizações políticas3917554000146.0Direção Municipal/Comissão ProvisóriaAVANTE - RIO DE JANEIRO - RJ - MUNICIPALMMunicipalRJ60011RIO DE JANEIRO<NA><NA><NA><NA>70AVANTEAVANTE<NA><NA>3025199529/09/2022ADESIVO PERFURADO 40X22125.04